Update ADAM safety, UI, and model workflows

.gitattributes

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.gitignore

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-# Python and test caches
 __pycache__/
 *.py[cod]
 .pytest_cache/
 .venv/
 venv/
-
-# Local application state and user-connected tools
-config/settings.json
-config/settings.local.json
-config/external_tools.json
-!config/external_tools.json
-data/*
-!data/.gitkeep
-logs/
-
-# Generated datasets, models, and media
+logs/*.log
+data/
 ADAM_Datasets/
-artifacts/
 build/
 dist/
+hf-release-*/
+artifacts/
+config/settings.local.json
+config/settings.json
+*.tmp
+*.sqlite3
 *.safetensors
 *.ckpt
 *.pt
 *.pth
-*.bin
-*.onnx
-*.tmp
+LoRAModelsHere/
+LoRA StableDiffusionModels Here/

Launch ADAM.bat

@@ -1,61 +1,25 @@
 @echo off
 setlocal
 cd /d "%~dp0"
-python -c "import yt_dlp, cv2, numpy" 2>nul
+python -c "import PySide6, psutil, PIL" 2>nul
 if errorlevel 1 (
-  echo Installing the ADAM Video Dataset Collector requirements...
-  python -m pip install -r "%~dp0requirements.txt"
-  if errorlevel 1 goto :adam_dependency_error
-)
-python -c "import selenium, requests, PIL" 2>nul
-if errorlevel 1 (
-  echo Installing the Dataset Collector requirements for ADAM...
-  python -m pip install -r "D:\Users\PlayRobloxAllDay\Desktop\Programs\GoogleImageDatasetCollector\requirements.txt"
-  if errorlevel 1 goto :dependency_error
-)
-python -c "import datasets, diffusers, transformers, accelerate, torch, torchvision" 2>nul
-if errorlevel 1 (
-  echo Installing the DDPM training requirements for ADAM...
-  python -m pip install -r "D:\Users\PlayRobloxAllDay\Desktop\Programs\DDPM\requirements.txt"
-  if errorlevel 1 goto :ddpm_dependency_error
+  echo ADAM's desktop requirements are missing from this Python environment.
+  echo Install them with:
+  echo   python -m pip install -r "%~dp0requirements.txt"
+  echo.
+  pause
+  exit /b 1
 )
+rem Optional trainers and collectors are connected through ADAM Settings.
+rem Their dependencies belong to their own environments, not startup.
 python main.py
 if errorlevel 1 (
   echo.
-  echo ADAM could not start. Install the requirements with:
-  echo   python -m pip install -r requirements.txt
+  echo ADAM could not start. Check the application log for details.
+  echo To install the application requirements, run:
+  echo   python -m pip install -r "%~dp0requirements.txt"
   echo.
   pause
+  exit /b 1
 )
-endlocal
-exit /b
-
-:adam_dependency_error
-echo.
-echo ADAM could not install its Video Dataset Collector requirements.
-echo Run this command with the same Python used to start ADAM:
-echo   python -m pip install -r "%~dp0requirements.txt"
-echo.
-pause
-endlocal
-exit /b
-
-:dependency_error
-echo.
-echo ADAM could not install the Dataset Collector requirements.
-echo Run this command and then launch ADAM again:
-echo   python -m pip install -r "D:\Users\PlayRobloxAllDay\Desktop\Programs\GoogleImageDatasetCollector\requirements.txt"
-echo.
-pause
-endlocal
-exit /b
-
-:ddpm_dependency_error
-echo.
-echo ADAM could not install the DDPM training requirements.
-echo Run this command and then launch ADAM again:
-echo   python -m pip install -r "D:\Users\PlayRobloxAllDay\Desktop\Programs\DDPM\requirements.txt"
-echo.
-pause
-endlocal
-exit /b
+endlocal

README.md

@@ -10,44 +10,385 @@ tags:
 
 # ADAM — AI Development and Automation Manager
 
-ADAM is a local Windows desktop hub for organizing AI image-development workflows. It helps you prepare and review datasets, connect your own training tools, plan runs with explicit approval, monitor jobs, and keep a history of your own assets.
+ADAM is a local, safety-first desktop hub for orchestrating AI project tools.
+It includes registered dataset, DDPM, and SDXL LoRA workflows with background
+planning, approval gates, progress reporting, and persistent asset history.
 
-## What is included
+![ADAM command center](docs/screenshots/command-center.png)
 
-- ADAM source code and the built-in tool registry.
-- A clean, empty external-tool registry.
-- No model weights, LoRAs, checkpoints, datasets, generated images, job history, personal paths, API keys, or local settings.
+Dataset preparation, captioning, and preview placeholders remain clearly marked
+as demo tools. The connected Dataset Collector, DDPM trainer, and Local SDXL
+LoRA Trainer use real adapters and never fall back to simulated training.
 
-## Requirements
+Existing program folders can be connected from **Settings → Tool folders**.
+ADAM stores only the path and scans for likely entry points; it does not copy or
+modify the external project. Folder assignments can also be pasted into chat:
 
-- Windows 10/11
-- Python 3.10 or later
-- Optional: NVIDIA GPU for compatible training workflows
-- Optional: Ollama for local chat assistance
+```text
+DDPM Trainer: D:\AI\DDPM
+Flow Matching Trainer: D:\AI\FlowMatchImageGenerator
+```
+
+On a new computer, install `requirements.txt` in your chosen Python environment
+before running `Launch ADAM.bat`. The launcher checks desktop dependencies and
+does not install packages automatically or depend on the developer's personal
+trainer folders. Install each optional trainer's dependencies according to that
+tool's setup instructions before using its ADAM workflow.
+
+Remote access is disabled by default. Devices with an access token can browse
+datasets, edit captions/review marks and submit work. Only enable it for trusted
+devices. The desktop **Allow remote job controls and approval changes** setting
+also permits remote confirmation, stopping and retrying jobs. A remote browser
+can enable training auto-approval only after that desktop permission is granted;
+it can always turn auto-approval off. Saved token changes and disabled access
+take effect for new requests without restarting the server.
+
+Use private Tailscale access for connections beyond a trusted local network;
+the built-in HTTP listener does not provide transport encryption by itself.
+Phone URLs and QR codes contain the access token and should be treated as
+credentials. Remote commands require JSON, have bounded request sizes and
+connection counts, and reject cross-site browser submissions. These controls
+do not sandbox installed Python plugins or connected trainers: install only
+code you trust.
+
+Detection does not automatically authorize training. A real training adapter
+remains gated until its dataset, model name, run settings, and output location
+are explicit.
+
+## Training agents
+
+![ADAM model creation settings](docs/screenshots/create-model.png)
+
+ADAM's training lifecycle is divided into four explainable responsibilities:
+
+- **EVE** reviews dataset membership and leaves uncertain images for the user.
+- **ORION** reviews planned epochs, batch size, resolution, image exposures, and
+  estimated optimizer steps. He can require approval but never silently changes
+  the requested settings. In the Model Creation Assistant, **ORION: apply a
+  starting recipe** fills a conservative, editable draft from the image count
+  and selected resolution before a plan is built.
+- **ATLAS** watches active training for non-finite loss, sustained critical GPU
+  temperature, critically low disk space, stalls, and large runtime overruns.
+  Critical conditions pause the trainer process tree so the user can inspect it.
+- **NOVA** examines available post-training previews and samples for unreadable
+  files and exact-looking duplicate collapse. Her report explicitly separates
+  technical sample health from subjective or subject-quality review.
+
+ORION, ATLAS, and NOVA reports are stored with each durable job record and are
+shown in Current Plan, Active Job, and Jobs / History respectively. ATLAS's
+default thresholds can be overridden in `config/settings.json` with the
+`atlas_*` settings defined in `adam/config.py`.
+
+Every new job passes through the shared preflight and ORION review before its
+queue state is chosen. Desktop plans, Remote prompts, and Remote training forms
+use the same review. Remote training auto-approval still applies to ordinary
+plans, but an ORION warning leaves the job awaiting explicit approval. Reviewing
+a plan does not change the requested training settings.
+
+## Real image collection
+
+When a valid Dataset Collector folder is connected, the `dataset_collector`
+registry entry uses ADAM's real visible-browser adapter. After plan approval it:
+
+- opens Bing Images in a normal visible Chrome window;
+- waits when consent/CAPTCHA/human-verification text is detected;
+- resumes automatically after the user resolves the page;
+- downloads valid images at least 256×256;
+- removes exact duplicate downloads;
+- writes a matching `.txt` caption beside every image; and
+- records URLs, captions, sources, and dimensions in `metadata.csv`.
+
+No CAPTCHA or website restriction is bypassed. Closing Chrome or stopping the
+job ends collection safely. A new timestamped dataset folder is used rather
+than overwriting an existing collection.
+
+ADAM keeps an incomplete DDPM request in conversation memory. A follow-up such
+as `dataset folder Mario, model name Mario V2, epoch count 100, output D:\Runs`
+fills the pending fields and validates named datasets against the connected
+collector. It will not start if the dataset cannot be found.
+
+## Showcase videos
+
+The **Showcase Video** workspace creates a finished MP4 directly from completed
+DDPM and Flow Matching models. Select and reorder the models, choose 12–24
+images per model, a 3-, 4-, or 5-second image duration, shared steps and aspect
+ratio, provider-compatible samplers, seed, and 720p or 1080p output. ADAM runs
+the image batches sequentially and then renders a request-list interface that
+tracks the active model, image number, trainer, steps, sampler, and aspect ratio.
+LoRA models are intentionally excluded from this streamlined workflow.
+
+When Ollama is reachable, messages that are not workflow commands receive a
+short conversational answer. Ollama may explain or plan, but it still cannot
+bypass the registry or confirmation gates.
+
+## Web search in Chat Mode
 
-## Install and run
+Chat Mode can give local Ollama current web context without an API key. Enable
+it in **Settings → Planning model**, then ask naturally, for example:
+
+```text
+Search the web for Dandy's World character ideas.
+What are the latest Ollama release notes?
+Look up a reference for a cyberpunk city character.
+```
+
+ADAM sends only that search query to Bing's public results feed, reads the
+result titles and snippets,
+and passes up to five titles, snippets, and links to Ollama. It does not open
+the result pages, download anything, or let web content run tools. Results are
+untrusted reference material, so ADAM is instructed to cite the links and flag
+uncertainty. Disable the setting to keep Chat Mode fully local.
+
+When you explicitly ask ADAM to **read**, **open**, or **research** result links,
+it can read up to three public HTML/text pages and give Ollama short extracts.
+For example: `Search the web for Undertale character ideas and read the most
+relevant links.` Direct links can be read with `Read https://example.com/ and
+summarize it.` Private/local addresses, non-web protocols, oversized pages,
+downloads, and more than three pages are blocked. This control can be disabled
+in Settings.
+
+Planning runs away from the interface thread, and conversational Ollama output
+is streamed into the chat. ADAM validates training commands against a strict
+schema and each registered trainer's declared capabilities before offering a
+job.
+
+In **Settings → Planning model**, **Chat response length** sets the maximum
+number of generated tokens for a Chat Mode reply. Higher values allow longer
+research summaries but use more time and GPU memory. The default is 1,024,
+which gives Qwen3 enough room to reason and still produce a visible response.
+
+ADAM stores friendly dataset/model names, paths, trainer types, epochs, and
+resume checkpoints in `data/assets.json`. Requests such as:
+
+```text
+From the Mario dataset, train it on a DDPM for 300 epochs.
+With the Mario dataset, train it on a LoRA for 100 epochs.
+Continue the Mario model from the DDPM for 50 epochs.
+```
+
+are resolved to real paths before approval. Continuation is offered only when a
+compatible checkpoint exists. New DDPM runs retain the latest resume checkpoint.
+
+## Run
 
 ```powershell
-git clone https://huggingface.co/SyntheticMDProductions/AI_Development_Automation_Manager
-cd AI_Development_Automation_Manager
-python -m pip install -r requirements.txt
 python main.py
 ```
 
-On first launch, ADAM creates your personal `config/settings.json` automatically. In **Settings → Tool folders**, connect the local projects, datasets, and models that you own and want ADAM to manage. ADAM does not bundle or download model weights for you.
+On Windows, you can also double-click `Launch ADAM.bat`.
 
-## Notes for users
+The app requires Python 3.10+ and PySide6. Optional integrations use `psutil`
+for system information and `pynvml` for NVIDIA GPU information.
 
-- Training and collection plans require approval before ADAM starts them.
-- You are responsible for the licenses, permissions, and rights for any datasets, models, and third-party tools you connect.
-- This repository is a downloadable desktop application. It is not a hosted Hugging Face Space or an inference model.
+```powershell
+python -m pip install -r requirements.txt
+```
 
-## Development
+Try:
 
-```powershell
-python -m pytest -q
+- Click **Create a model…** in Trainer Mode for the guided Model Creation Assistant.
+- `Adam, train a LoRA of Hatsune Miku`
+- `Adam, collect a dataset of liminal spaces`
+- `Adam, generate previews`
+- `Adam, check GPU status`
+- `From the Mario dataset, train it on a DDPM for 300 epochs`
+- `With the Mario dataset, train it on a LoRA for 100 epochs`
+
+Training and large collection plans are never started until you approve the
+plan. All actions are recorded in `logs/adam.log`, while project artifacts live
+under `data/projects/`.
+
+The Model Creation Assistant can start from a built-in Character LoRA, Style
+LoRA, DDPM, or Flow Matching preset. It can create a dataset or select a
+registered one, recommends starting values, and saves personal presets. The
+result still goes through ADAM's normal validated planner and approval gate.
+Use **+ Add model** to build a multi-model training batch. Each wide model tab
+keeps its own dataset, trainer, name, and settings; the minus button removes an
+unwanted model, and tabs can be dragged to change the run order. ADAM validates
+all models, presents one combined approval plan, and runs them sequentially so
+only one training workflow uses the GPU at a time. A failed step stops the batch
+before a later model starts.
+Before approval, ADAM adds checks for connected tools, dataset contents, the
+LoRA base model, and output-drive free space. Completed dataset and training
+jobs also include a suggested next step.
+
+### Model Batch Builder
+
+Use **Create model batch…** to paste one requested subject per line. ADAM turns
+the list into editable model tabs, removes duplicate names, and lets the current
+trainer recipe be applied to any multi-selection of models. The batch is saved
+as a draft so it can be closed and resumed later.
+
+For a review-first workflow, choose **Collect missing datasets first**. This
+queues only sequential dataset collection and leaves training in the saved
+draft. After collection, reopen the draft, use **Find collected datasets**, and
+review each dataset in Training Studio. **Exclude rejected** moves rejected
+images out of the training folder into a recoverable quarantine, and **Restore
+excluded** reverses it. **Keep all images** marks the whole selected dataset as
+accepted in one action, after which individual bad images can still be rejected.
+Training remains locked until each model is explicitly
+marked as reviewed and ready. If every linked dataset is acceptable as-is,
+**Approve all datasets** marks the entire batch ready after one confirmation;
+it does not inspect individual images or apply pending rejection decisions.
+
+Completed Flow Matching models can be selected in **Fine-tune**. ADAM uses the
+saved Flow model folder as the continuation source, locks the continuation to
+the model's original resolution, and writes the fine-tuned result to a new
+output folder. This continues the saved weights while starting a fresh optimizer
+and learning-rate schedule; it does not overwrite the original model.
+
+## Training Studio
+
+The **Training Studio** turns completed work into a reviewable experiment loop:
+
+- **Datasets** provides an image gallery, keep/reject decisions, caption editing,
+  exact duplicate detection, and visually similar duplicate candidates.
+- **Experiments** compares job settings and outcomes, opens outputs, marks a
+  preferred model, and converts successful settings into reusable recipes.
+- **Checkpoint Lab** browses model checkpoints and output images, records
+  consistent prompt/seed evaluations, and sends preview requests through the
+  normal approval-aware planner.
+- **Recipes** preserves training starting points and can import or export
+  portable JSON recipe files.
+
+### EVE AI Dataset Review
+
+In Training Studio → Datasets, **EVE AI Review…** performs a local reference-
+guided visual review. Add one or more good reference images and optional bad
+references, then choose Keep and Reject confidence thresholds. EVE uses a small
+DINOv2 vision model to divide the selected dataset into **Keep**, **Reject**, and
+**Uncertain** galleries with confidence scores. The model is downloaded once on
+first use and subsequent analysis stays local.
+
+Nothing is applied automatically. Inspect both sides, double-click images for a
+full view, and move selected results between the three groups before choosing
+**Apply EVE review**. EVE's decisions remain ordinary Training Studio review
+marks: they can be manually changed, and rejected files are not moved until
+**Exclude rejected** is selected. The latest proposal is also saved under
+`data/eve_reviews/` for auditing. Use **Select all in current group** (or
+Ctrl/Shift selection) to move many images at once; EVE transfers only the
+chosen thumbnails so manual sorting stays responsive on large datasets.
+
+Training panels show elapsed time, a progress-based ETA, recent logs, and a
+loss sparkline when the connected trainer reports `loss`. Preflight summaries
+include clearly labelled workload, duration, VRAM, and disk estimates. These
+estimates are planning hints rather than hardware guarantees.
+
+Create a Model also supports live training previews with a configurable
+epoch interval, prompt, and reproducible seed for each model tab. While a
+training job is active, its newest 256×256 preview appears in the right sidebar
+with the source epoch and next scheduled preview. The full-size trainer output
+can be opened from the card. Built-in adapters may publish previews directly;
+registered DDPM, Flow, LoRA, APVD, MaskGit, and other trainers can also
+participate by writing conventionally named `preview`, `sample`, or `epoch`
+images beneath their declared output folder.
+
+## Generations
+
+The **Generations** workspace runs compatible registered image generators
+without opening their separate desktop interfaces. The connected DDPM and Flow
+Matching projects can generate from completed models with a reproducible seed,
+sampler or ODE method, step count, image count, and aspect ratio. Generation
+work uses the normal ADAM job queue, progress reporting, cancellation, and
+logging.
+
+Every completed batch is stored under `data/generations/` with its images and a
+`generation.json` sidecar. The history gallery can open an image or batch folder
+and restore the exact settings for another run. DDPM creative notes are stored
+with a batch for organization; they are not presented as text conditioning for
+an unconditional DDPM model.
+
+Generation history opens as automatic model folders. Each folder uses the
+registered model name and a recent generated image as its cover. Double-clicking
+a folder filters history to that model and selects its provider and model in the
+generation controls, so the next batch is generated into the same existing model
+directory. This view does not move or rewrite older generation files.
+
+**Generation Cycle…** selects multiple compatible completed models and queues
+one generation step per model. Choose images per model, a shared prompt or
+creative note, starting seed, slideshow duration, looping, fullscreen playback,
+and an optional model/trainer label. When the cycle finishes, ADAM opens the
+results as a local slideshow while preserving every ordinary generation record
+in history.
+
+If ADAM discovers a job interrupted by an unexpected shutdown, it offers to
+open Jobs & History. The previous record remains intact and can be retried as a
+new approval-gated job. Job logs can also be exported for troubleshooting.
+
+## Connect an existing tool
+
+ADAM supports importable Python functions and command-line Python scripts.
+For a no-code setup, open **Settings → External Tools → Add external tool**.
+Choose the program folder, select its training entry script and important
+configuration files, then review ADAM's static compatibility and safety report.
+The report covers:
+
+- detected command-line options and required inputs;
+- likely dataset formats;
+- output and checkpoint behavior;
+- progress reporting;
+- resume-training support; and
+- potentially risky operations visible in the selected entry script.
+
+The 1–10 rating measures how clearly the script fits ADAM's safe command-line
+contract. It is not a guarantee that third-party code is harmless. ADAM does
+not execute a script while scanning it, external tools cannot replace built-in
+registry entries, and every external-tool run requires explicit approval.
+
+After registration, a tool can be planned with a request such as:
+
+```text
+Run APVD Model Trainer with dataset=D:\DreamData, epochs=20, output=D:\APVD\output
 ```
 
-## License
+ADAM will ask for any required inputs that were omitted before it offers the
+approval plan.
+
+For manual registry configuration, edit the relevant item in
+`config/tools.json`:
+
+```json
+{
+  "backend": {
+    "type": "python",
+    "module": "my_tools.lora",
+    "function": "train"
+  },
+  "demo": false
+}
+```
+
+The function receives a `ToolContext` as its first argument and keyword
+arguments from the approved plan. This keeps training code in one place: your
+existing GUI and ADAM can both call the same backend.
+
+For scripts:
 
-ADAM is released under the [MIT License](LICENSE).
+```json
+{
+  "backend": {
+    "type": "script",
+    "path": "D:/AI/LoRATrainer/train.py"
+  },
+  "demo": false
+}
+```
+
+ADAM invokes scripts directly with the current Python interpreter, captures
+stdout/stderr, and never drives another GUI with mouse clicks.
+
+## Safety model
+
+- Plans are shown before execution.
+- Long, destructive, or high-volume work requires confirmation.
+- Unregistered tools cannot be invoked.
+- External paths and arguments are validated before execution.
+- The LLM may propose a plan, but only registered tools can execute it.
+- Pause, resume, and cancel controls are available for active jobs.
+- Every tool action and state transition is logged.
+
+## Tests
+
+```powershell
+python -m pytest -q
+```

adam/assets.py

@@ -17,6 +17,15 @@ def _normal(value: str) -> str:
     return re.sub(r"[^a-z0-9]+", " ", value.casefold()).strip()
 
 
+def _friendly_name(value: str, fallback: str) -> str:
+    text = str(value or "").strip()
+    if not text:
+        return fallback
+    if re.search(r"^[A-Za-z]:[\\/]", text) or "/" in text or "\\" in text:
+        return Path(text).name or fallback
+    return text
+
+
 @dataclass(slots=True)
 class Asset:
     id: str
@@ -28,6 +37,7 @@ class Asset:
     checkpoint: str = ""
     epochs: int = 0
     created_at: str = ""
+    metadata: dict[str, Any] | None = None
 
     @classmethod
     def from_dict(cls, payload: dict[str, Any]) -> "Asset":
@@ -41,6 +51,7 @@ class Asset:
             checkpoint=str(payload.get("checkpoint", "")),
             epochs=int(payload.get("epochs", 0) or 0),
             created_at=str(payload.get("created_at") or _now()),
+            metadata=dict(payload.get("metadata") or {}),
         )
 
 
@@ -82,6 +93,7 @@ class AssetRegistry:
         dataset_id: str = "",
         checkpoint: str = "",
         epochs: int = 0,
+        metadata: dict[str, Any] | None = None,
         persist: bool = True,
     ) -> Asset:
         resolved = str(Path(path).expanduser().resolve())
@@ -100,6 +112,10 @@ class AssetRegistry:
         asset.checkpoint = checkpoint
         asset.epochs = int(epochs)
         asset.created_at = asset.created_at or _now()
+        if metadata:
+            current = dict(asset.metadata or {})
+            current.update(metadata)
+            asset.metadata = current
         if existing is None:
             self.assets.insert(0, asset)
         if persist:
@@ -118,10 +134,14 @@ class AssetRegistry:
                     key: item[key]
                     for key in (
                         "kind", "name", "path", "trainer", "dataset_id",
-                        "checkpoint", "epochs",
+                        "checkpoint", "epochs", "metadata",
                     )
                     if key in item
                 }
+                if "trigger_word" in item:
+                    metadata = dict(values.get("metadata") or {})
+                    metadata["trigger_word"] = str(item.get("trigger_word") or "")
+                    values["metadata"] = metadata
                 dataset_path = str(item.get("dataset_path", ""))
                 if item.get("kind") == "model" and dataset_path and Path(dataset_path).is_dir():
                     dataset = self.register(
@@ -151,7 +171,18 @@ class AssetRegistry:
         folders = config.get("tool_folders", {})
         if not isinstance(folders, dict):
             return
+        folders = dict(folders)
         app_root = self.path.parent.parent
+        if not folders.get("oasis_trainer"):
+            try:
+                external = json.loads((app_root / "config" / "external_tools.json").read_text(encoding="utf-8"))
+            except (OSError, ValueError, TypeError, json.JSONDecodeError):
+                external = {}
+            for entry in external.get("tools", []) if isinstance(external, dict) else []:
+                if isinstance(entry, dict) and entry.get("id") == "external_oasis_game_trainer":
+                    root = str(entry.get("backend", {}).get("root", ""))
+                    if root:
+                        folders["oasis_trainer"] = root
         external_lora_root = app_root / "LoRAModelsHere"
         if external_lora_root.is_dir():
             for path in external_lora_root.rglob("*.safetensors"):
@@ -194,6 +225,7 @@ class AssetRegistry:
             ("ddpm", "ddpm_trainer", "output"),
             ("lora", "lora_trainer", "output"),
             ("flow", "flow_trainer", "output_flow_models"),
+            ("oasis", "oasis_trainer", "output_action_flow_models"),
         ):
             root = Path(str(folders.get(folder_name, ""))) / output_name
             if not root.is_dir():
@@ -219,6 +251,7 @@ class AssetRegistry:
                         else -1,
                     )
                 elif trainer == "lora":
+                    trigger_word = ""
                     checkpoints = sorted(
                         (
                             path for path in folder.glob("*.safetensors")
@@ -228,7 +261,12 @@ class AssetRegistry:
                     )
                     if checkpoints:
                         name = checkpoints[-1].stem.removesuffix("_cancelled")
-                else:
+                    try:
+                        metadata = json.loads((folder / "model_info.json").read_text(encoding="utf-8"))
+                        trigger_word = str(metadata.get("trigger_word") or "")
+                    except (OSError, ValueError, TypeError, json.JSONDecodeError):
+                        trigger_word = ""
+                elif trainer == "flow":
                     checkpoints = []
                     try:
                         metadata = json.loads(
@@ -242,8 +280,24 @@ class AssetRegistry:
                         dataset_path = flow_datasets.get(str(folder.resolve()), "")
                     except (OSError, ValueError, TypeError, json.JSONDecodeError):
                         continue
+                else:
+                    checkpoints = []
+                    try:
+                        metadata = json.loads(
+                            (folder / "action_flow_model_info.json").read_text(encoding="utf-8")
+                        )
+                        if metadata.get("model_type") != "action_conditioned_rectified_flow_video":
+                            continue
+                        if not (folder / "unet" / "config.json").is_file():
+                            continue
+                        name = _friendly_name(
+                            str(metadata.get("model_name") or metadata.get("name") or name),
+                            folder.name,
+                        )
+                    except (OSError, ValueError, TypeError, json.JSONDecodeError):
+                        continue
                 checkpoint = (
-                    str(folder) if trainer == "flow" else str(checkpoints[-1]) if checkpoints else ""
+                    str(folder) if trainer in {"flow", "oasis"} else str(checkpoints[-1]) if checkpoints else ""
                 )
                 dataset_id = ""
                 if dataset_path and Path(dataset_path).is_dir():
@@ -261,8 +315,27 @@ class AssetRegistry:
                     trainer=trainer,
                     dataset_id=dataset_id,
                     checkpoint=checkpoint,
+                    metadata=({"trigger_word": trigger_word or name} if trainer == "lora" else None),
                     persist=False,
                 )
+        try:
+            from adam.dataset_registry import DatasetRegistry
+
+            dataset_registry = DatasetRegistry(app_root, config)
+            valid_location_ids = {location.id for location in dataset_registry.known_locations()}
+            self.assets = [
+                item for item in self.assets
+                if not (
+                    item.kind == "dataset"
+                    and isinstance(item.metadata, dict)
+                    and item.metadata.get("dataset_registry_source") in {"adam", "tool"}
+                    and item.metadata.get("dataset_location_id")
+                    and item.metadata.get("dataset_location_id") not in valid_location_ids
+                )
+            ]
+            dataset_registry.discover_into_assets(self, persist=False)
+        except Exception:
+            pass
         self.save()
 
     def _flow_dataset_paths(self) -> dict[str, str]:

adam/atlas.py

@@ -2,7 +2,9 @@ from __future__ import annotations
 
 import math
 import re
+import shutil
 import time
+from pathlib import Path
 from dataclasses import dataclass
 from datetime import datetime
 from typing import Any
@@ -57,17 +59,35 @@ class AtlasSupervisor:
             return AtlasDecision("critical", f"GPU temperature remained at {temperature:.0f}°C. ATLAS paused the job.", "pause")
 
         free_disk = max(0.0, snapshot.disk_total_gb - snapshot.disk_used_gb)
-        state["disk_samples"] = state["disk_samples"] + 1 if snapshot.disk_total_gb and free_disk <= self.critical_disk_gb else 0
+        disk_known = bool(snapshot.disk_total_gb)
+        disk_label = "monitored drive"
+        output = job.plan.steps[job.current_step].arguments.get("output_dir") or job.output_folder
+        disk_error = False
+        if output:
+            disk_label = "output drive"
+            try:
+                target = Path(str(output)).expanduser().resolve()
+                while not target.exists() and target != target.parent:
+                    target = target.parent
+                usage = shutil.disk_usage(target)
+                free_disk = usage.free / (1024 ** 3)
+                disk_known = True
+            except (OSError, ValueError):
+                disk_known = False
+                disk_error = True
+        state["disk_samples"] = state["disk_samples"] + 1 if disk_known and free_disk <= self.critical_disk_gb else 0
         if state["disk_samples"] >= 2:
-            return AtlasDecision("critical", f"Only {free_disk:.1f} GB remains on the output drive. ATLAS paused the job.", "pause")
+            return AtlasDecision("critical", f"Only {free_disk:.1f} GB remains on the {disk_label}. ATLAS paused the job.", "pause")
 
         stalled_minutes = (moment - state["changed_at"]) / 60
         if stalled_minutes >= self.stall_minutes and snapshot.gpu_percent < 5:
             return AtlasDecision("warning", f"No recorded progress and little GPU activity for {stalled_minutes:.0f} minutes. Check the trainer.")
         if temperature is not None and temperature >= self.warning_temp:
             return AtlasDecision("warning", f"GPU temperature is elevated at {temperature:.0f}°C; ATLAS is watching it closely.")
-        if snapshot.disk_total_gb and free_disk <= self.warning_disk_gb:
-            return AtlasDecision("warning", f"Output drive space is getting low ({free_disk:.1f} GB free).")
+        if disk_error:
+            return AtlasDecision("warning", "ATLAS could not check the output drive's free space. Check that the output location is available.")
+        if disk_known and free_disk <= self.warning_disk_gb:
+            return AtlasDecision("warning", f"Space on the {disk_label} is getting low ({free_disk:.1f} GB free).")
         if snapshot.memory_percent >= 95:
             return AtlasDecision("warning", f"System memory usage is very high at {snapshot.memory_percent:.0f}%.")
 

adam/commands.py

@@ -1,8 +1,11 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+from pathlib import Path
 from typing import Any
 
+from adam.model_plugins import ModelPluginRegistry, validate_settings
+
 
 class CommandValidationError(ValueError):
     pass
@@ -18,13 +21,14 @@ class TrainingCommand:
     output: str = "default output"
     resume_from: str = ""
     base_model: str = ""
+    trigger_word: str = ""
     training_options: dict[str, Any] | None = None
 
     @classmethod
     def from_dict(cls, payload: dict[str, Any]) -> "TrainingCommand":
         allowed = {
             "action", "trainer", "dataset", "model_name", "epochs", "output",
-            "resume_from", "base_model",
+            "resume_from", "base_model", "trigger_word",
             "training_options",
         }
         unknown = set(payload) - allowed
@@ -42,42 +46,63 @@ class TrainingCommand:
                 output=str(payload.get("output", "default output")).strip(),
                 resume_from=str(payload.get("resume_from", "")).strip(),
                 base_model=str(payload.get("base_model", "")).strip(),
+                trigger_word=str(
+                    payload.get("trigger_word")
+                    or (payload.get("training_options") or {}).get("trigger_word")
+                    or ""
+                ).strip(),
                 training_options=dict(payload.get("training_options") or {}),
             )
         except (TypeError, ValueError) as exc:
             raise CommandValidationError("Training command fields have invalid types.") from exc
         if command.action not in {"train", "resume_training"}:
             raise CommandValidationError("Training action must be train or resume_training.")
-        if command.trainer not in {"ddpm", "lora", "flow"}:
-            raise CommandValidationError("Trainer must be ddpm, flow, or lora.")
+        plugin_schema = ModelPluginRegistry(Path.cwd()).training_schema(command.trainer)
+        if command.trainer not in {"ddpm", "lora", "flow"} and not plugin_schema:
+            raise CommandValidationError("Trainer must be a discovered model plugin.")
         if not command.dataset or not command.model_name:
             raise CommandValidationError("Dataset and model name are required.")
         if not 1 <= command.epochs <= 100_000:
             raise CommandValidationError("Epoch count must be between 1 and 100000.")
         if command.action == "resume_training" and not command.resume_from:
             raise CommandValidationError("Resume training requires an explicit checkpoint.")
+        if command.trainer == "lora":
+            trigger = command.trigger_word or command.model_name
+            if len(trigger) > 128 or any(char in trigger for char in '<>:"/\\|?*\x00'):
+                raise CommandValidationError("LoRA trigger word must be short text without reserved characters.")
         command._validate_options()
         return command
 
     def _validate_options(self) -> None:
         options = self.training_options or {}
-        allowed = {
-            "ddpm": {
-                "resolution", "batch_size", "learning_rate", "gradient_accumulation_steps",
-                "dataloader_num_workers", "mixed_precision", "save_every", "preview_steps",
-                "training_intensity", "preview_enabled", "preview_every", "preview_prompt",
-                "preview_seed",
-            },
-            "flow": {
-                "resolution", "batch_size", "learning_rate", "gradient_accumulation",
-                "workers", "mixed_precision", "save_every", "preview_every", "preview_steps",
-                "gradient_checkpointing", "preview_enabled", "preview_prompt", "preview_seed",
-            },
-            "lora": {"preview_enabled", "preview_every", "preview_prompt", "preview_seed"},
-        }[self.trainer]
+        schema = ModelPluginRegistry(Path.cwd()).training_schema(self.trainer)
+        allowed = set(schema)
+        if not allowed:
+            allowed = {
+                "ddpm": {
+                    "resolution", "batch_size", "learning_rate", "gradient_accumulation_steps",
+                    "dataloader_num_workers", "mixed_precision", "save_every", "preview_steps",
+                    "training_intensity", "preview_enabled", "preview_every", "preview_prompt",
+                    "preview_seed",
+                },
+                "flow": {
+                    "resolution", "batch_size", "learning_rate", "gradient_accumulation",
+                    "workers", "mixed_precision", "save_every", "preview_every", "preview_steps",
+                    "gradient_checkpointing", "preview_enabled", "preview_prompt", "preview_seed",
+                },
+                "lora": {"preview_enabled", "preview_every", "preview_prompt", "preview_seed", "trigger_word"},
+            }[self.trainer]
         unknown = set(options) - allowed
         if unknown:
             raise CommandValidationError(f"Unsupported {self.trainer} training options: {', '.join(sorted(unknown))}")
+        if schema:
+            errors = validate_settings(
+                {key: spec for key, spec in schema.items() if key in options},
+                options,
+            )
+            if errors:
+                raise CommandValidationError(" ".join(errors))
+            return
         integer_ranges = {
             "resolution": (64, 512), "batch_size": (1, 64),
             "gradient_accumulation_steps": (1, 64), "gradient_accumulation": (1, 64),

adam/config.py

@@ -26,12 +26,21 @@ DEFAULT_SETTINGS: dict[str, Any] = {
     "demo_step_delay": 0.24,
     "max_dataset_images_without_confirmation": 100,
     "training_presets": {},
+    "remote_access": {
+        "enabled": False,
+        "bind_address": "127.0.0.1",
+        "port": 8765,
+        "token": "",
+        "allow_job_control": False,
+        "auto_approve_training": False,
+    },
     "tool_folders": {
         "dataset_collector": "",
         "caption_generator": "",
         "lora_trainer": "",
         "ddpm_trainer": "",
         "flow_trainer": "",
+        "oasis_trainer": "",
         "preview_generator": "",
     },
 }

adam/dataset_lab.py

@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import hashlib
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+
+
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
+VIDEO_EXTENSIONS = {".mp4", ".mov", ".mkv", ".webm", ".avi"}
+TEXT_EXTENSIONS = {".txt", ".caption", ".jsonl", ".json"}
+
+
+@dataclass(slots=True)
+class DatasetItem:
+    path: str
+    kind: str
+    size_bytes: int
+    width: int = 0
+    height: int = 0
+    caption_path: str = ""
+    duplicate_key: str = ""
+
+
+@dataclass(slots=True)
+class DatasetReport:
+    path: str
+    total_files: int = 0
+    image_count: int = 0
+    video_count: int = 0
+    text_count: int = 0
+    caption_count: int = 0
+    missing_caption_count: int = 0
+    duplicate_groups: int = 0
+    dimensions: dict[str, int] = field(default_factory=dict)
+    extensions: dict[str, int] = field(default_factory=dict)
+    items: list[DatasetItem] = field(default_factory=list)
+    warnings: list[str] = field(default_factory=list)
+
+    def to_dict(self) -> dict:
+        return asdict(self)
+
+
+def _hash_file(path: Path) -> str:
+    digest = hashlib.sha1()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def scan_dataset(path: str | Path, *, limit: int = 500) -> DatasetReport:
+    root = Path(path).expanduser().resolve()
+    report = DatasetReport(path=str(root))
+    if not root.is_dir():
+        report.warnings.append("Dataset folder does not exist.")
+        return report
+    hashes: dict[str, int] = {}
+    try:
+        files = [item for item in root.rglob("*") if item.is_file()]
+    except OSError as exc:
+        report.warnings.append(f"Dataset could not be scanned: {exc}")
+        return report
+    report.total_files = len(files)
+    for item in files:
+        suffix = item.suffix.casefold()
+        report.extensions[suffix or "(none)"] = report.extensions.get(suffix or "(none)", 0) + 1
+        if suffix in IMAGE_EXTENSIONS:
+            report.image_count += 1
+            if not any(item.with_suffix(ext).is_file() for ext in (".txt", ".caption")):
+                report.missing_caption_count += 1
+        elif suffix in VIDEO_EXTENSIONS:
+            report.video_count += 1
+        if suffix in TEXT_EXTENSIONS:
+            report.text_count += 1
+            if suffix in {".txt", ".caption"}:
+                report.caption_count += 1
+    for item in files[: max(1, limit)]:
+        suffix = item.suffix.casefold()
+        kind = "other"
+        width = height = 0
+        caption_path = ""
+        duplicate_key = ""
+        if suffix in IMAGE_EXTENSIONS:
+            kind = "image"
+            caption = next((item.with_suffix(ext) for ext in (".txt", ".caption") if item.with_suffix(ext).is_file()), None)
+            caption_path = str(caption) if caption else ""
+            try:
+                from PIL import Image
+
+                with Image.open(item) as image:
+                    width, height = image.size
+                label = f"{width}x{height}"
+                report.dimensions[label] = report.dimensions.get(label, 0) + 1
+            except Exception:
+                pass
+            try:
+                duplicate_key = _hash_file(item)
+                hashes[duplicate_key] = hashes.get(duplicate_key, 0) + 1
+            except OSError:
+                duplicate_key = ""
+        elif suffix in VIDEO_EXTENSIONS:
+            kind = "video"
+        elif suffix in TEXT_EXTENSIONS:
+            kind = "text"
+        try:
+            size = item.stat().st_size
+        except OSError:
+            size = 0
+        report.items.append(
+            DatasetItem(
+                path=str(item),
+                kind=kind,
+                size_bytes=size,
+                width=width,
+                height=height,
+                caption_path=caption_path,
+                duplicate_key=duplicate_key,
+            )
+        )
+    if report.total_files > limit:
+        report.warnings.append(f"Showing first {limit:,} files; totals still include all files.")
+    report.duplicate_groups = sum(1 for count in hashes.values() if count > 1)
+    if report.image_count and report.missing_caption_count:
+        report.warnings.append(f"{report.missing_caption_count:,} sampled image(s) do not have sidecar captions.")
+    if report.duplicate_groups:
+        report.warnings.append(f"{report.duplicate_groups:,} duplicate image group(s) found in the sample.")
+    return report

adam/dataset_registry.py

@@ -0,0 +1,484 @@
+from __future__ import annotations
+
+import hashlib
+import json
+import threading
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from typing import Any, TYPE_CHECKING
+from uuid import uuid4
+
+from adam.dataset_lab import IMAGE_EXTENSIONS, TEXT_EXTENSIONS, VIDEO_EXTENSIONS
+
+if TYPE_CHECKING:
+    from adam.assets import Asset, AssetRegistry
+
+
+DATASET_MARKERS = {
+    "dataset_manifest.json",
+    "metadata",
+    "frames",
+    "videos",
+    "captions",
+    "actions.jsonl",
+    "actions.csv",
+}
+SCAN_LIMIT = 20_000
+ASYNC_REFRESH_AFTER = timedelta(minutes=30)
+_scan_lock = threading.Lock()
+_active_scans: set[str] = set()
+
+
+def _now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _path_key(path: str | Path) -> str:
+    resolved = str(Path(path).expanduser().resolve())
+    return hashlib.sha1(resolved.casefold().encode("utf-8")).hexdigest()[:16]
+
+
+def _atomic_json(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_suffix(path.suffix + ".tmp")
+    temporary.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+    temporary.replace(path)
+
+
+def _date(value: str) -> datetime | None:
+    try:
+        return datetime.fromisoformat(value)
+    except (TypeError, ValueError):
+        return None
+
+
+@dataclass(slots=True)
+class DatasetLocation:
+    id: str
+    name: str
+    path: str
+    source: str = "user"
+    created_at: str = field(default_factory=_now)
+    last_seen_at: str = ""
+    exists: bool = True
+
+    @classmethod
+    def from_dict(cls, payload: dict[str, Any]) -> "DatasetLocation":
+        return cls(
+            id=str(payload.get("id") or _path_key(str(payload.get("path", "")))),
+            name=str(payload.get("name") or Path(str(payload.get("path", ""))).name or "Datasets"),
+            path=str(payload.get("path", "")),
+            source=str(payload.get("source") or "user"),
+            created_at=str(payload.get("created_at") or _now()),
+            last_seen_at=str(payload.get("last_seen_at") or ""),
+            exists=bool(payload.get("exists", True)),
+        )
+
+
+@dataclass(slots=True)
+class DatasetRecord:
+    id: str
+    name: str
+    path: str
+    source: str = "asset"
+    location_id: str = ""
+    favorite: bool = False
+    last_used_at: str = ""
+    discovered_at: str = field(default_factory=_now)
+    scanned_at: str = ""
+    exists: bool = True
+    item_count: int = 0
+    image_count: int = 0
+    video_count: int = 0
+    caption_count: int = 0
+    missing_caption_count: int = 0
+    sample_image: str = ""
+    dataset_format: str = "Unknown"
+    warnings: list[str] = field(default_factory=list)
+
+    @classmethod
+    def from_dict(cls, payload: dict[str, Any]) -> "DatasetRecord":
+        return cls(
+            id=str(payload.get("id") or _path_key(str(payload.get("path", "")))),
+            name=str(payload.get("name") or Path(str(payload.get("path", ""))).name or "Dataset"),
+            path=str(payload.get("path", "")),
+            source=str(payload.get("source") or "asset"),
+            location_id=str(payload.get("location_id") or ""),
+            favorite=bool(payload.get("favorite", False)),
+            last_used_at=str(payload.get("last_used_at") or ""),
+            discovered_at=str(payload.get("discovered_at") or _now()),
+            scanned_at=str(payload.get("scanned_at") or ""),
+            exists=bool(payload.get("exists", True)),
+            item_count=int(payload.get("item_count", 0) or 0),
+            image_count=int(payload.get("image_count", 0) or 0),
+            video_count=int(payload.get("video_count", 0) or 0),
+            caption_count=int(payload.get("caption_count", 0) or 0),
+            missing_caption_count=int(payload.get("missing_caption_count", 0) or 0),
+            sample_image=str(payload.get("sample_image") or ""),
+            dataset_format=str(payload.get("dataset_format") or "Unknown"),
+            warnings=[str(item) for item in payload.get("warnings", []) if str(item)],
+        )
+
+
+class DatasetRegistry:
+    """Persistent ADAM-aware index of known dataset locations and datasets."""
+
+    def __init__(self, root: Path, config: Any | None = None) -> None:
+        self.root = root.resolve()
+        self.path = self.root / "data" / "dataset_registry.json"
+        self.config = config
+        self.locations: list[DatasetLocation] = []
+        self.datasets: dict[str, DatasetRecord] = {}
+        self.load()
+
+    def load(self) -> None:
+        try:
+            payload = json.loads(self.path.read_text(encoding="utf-8"))
+        except (OSError, ValueError, TypeError, json.JSONDecodeError):
+            payload = {}
+        self.locations = [
+            DatasetLocation.from_dict(item)
+            for item in payload.get("locations", [])
+            if isinstance(item, dict)
+        ]
+        self.datasets = {
+            str(key): DatasetRecord.from_dict(value)
+            for key, value in (payload.get("datasets", {}) or {}).items()
+            if isinstance(value, dict)
+        }
+
+    def save(self) -> None:
+        _atomic_json(
+            self.path,
+            {
+                "locations": [asdict(item) for item in self.locations],
+                "datasets": {key: asdict(value) for key, value in self.datasets.items()},
+            },
+        )
+
+    def register_location(self, path: str | Path, *, name: str = "", source: str = "user") -> DatasetLocation:
+        resolved = Path(path).expanduser().resolve()
+        if not resolved.is_dir():
+            raise ValueError("Choose an existing dataset folder.")
+        key = _path_key(resolved)
+        existing = next((item for item in self.locations if item.id == key), None)
+        if existing is None:
+            existing = DatasetLocation(
+                id=key,
+                name=name.strip() or resolved.name or str(resolved),
+                path=str(resolved),
+                source=source,
+            )
+            self.locations.insert(0, existing)
+        else:
+            existing.name = name.strip() or existing.name
+            existing.path = str(resolved)
+            existing.source = source or existing.source
+        existing.exists = True
+        existing.last_seen_at = _now()
+        self.save()
+        return existing
+
+    def remove_location(self, location_id: str) -> bool:
+        before = len(self.locations)
+        self.locations = [item for item in self.locations if item.id != location_id]
+        changed = len(self.locations) != before
+        if changed:
+            self.save()
+        return changed
+
+    def known_locations(self) -> list[DatasetLocation]:
+        locations = list(self.locations)
+        by_path = {Path(item.path).expanduser().resolve(): item for item in locations if item.path}
+        for path, name, source in self._automatic_location_candidates():
+            try:
+                resolved = path.expanduser().resolve()
+            except OSError:
+                continue
+            if resolved in by_path:
+                continue
+            locations.append(
+                DatasetLocation(
+                    id=_path_key(resolved),
+                    name=name or resolved.name or str(resolved),
+                    path=str(resolved),
+                    source=source,
+                    exists=resolved.is_dir(),
+                    last_seen_at=_now() if resolved.is_dir() else "",
+                )
+            )
+        return locations
+
+    def discover_into_assets(self, assets: "AssetRegistry", *, persist: bool = False) -> list["Asset"]:
+        discovered: list[Asset] = []
+        records = self.discover(asset_registry=assets, refresh_missing=False)
+        for record in records:
+            if not record.exists:
+                continue
+            asset = assets.register(
+                kind="dataset",
+                name=record.name,
+                path=record.path,
+                metadata={
+                    "dataset_registry_source": record.source,
+                    "dataset_location_id": record.location_id,
+                },
+                persist=False,
+            )
+            discovered.append(asset)
+        if persist and discovered:
+            assets.save()
+        return discovered
+
+    def discover(
+        self,
+        *,
+        asset_registry: "AssetRegistry | None" = None,
+        refresh_missing: bool = True,
+    ) -> list[DatasetRecord]:
+        self.load()
+        changed = False
+        locations = self.known_locations()
+        known_location_ids = {location.id for location in locations}
+        for key, record in list(self.datasets.items()):
+            if record.source in {"adam", "tool"} and record.location_id and record.location_id not in known_location_ids:
+                del self.datasets[key]
+                changed = True
+        for location in locations:
+            exists = Path(location.path).is_dir()
+            if location.source == "user":
+                stored = next((item for item in self.locations if item.id == location.id), None)
+                if stored:
+                    stored.exists = exists
+                    stored.last_seen_at = _now() if exists else stored.last_seen_at
+                    changed = True
+            if not exists:
+                continue
+            for candidate in self._dataset_candidates(Path(location.path)):
+                record = self._cached_or_sampled(candidate, source=location.source, location_id=location.id)
+                self.datasets[record.id] = record
+                changed = True
+        if asset_registry is not None:
+            for asset in getattr(asset_registry, "assets", []):
+                if getattr(asset, "kind", "") != "dataset":
+                    continue
+                record = self._cached_or_sampled(Path(asset.path), source="asset", location_id="")
+                record.name = asset.name or record.name
+                self.datasets[record.id] = record
+                changed = True
+        for record in self.datasets.values():
+            record.exists = Path(record.path).is_dir()
+            if refresh_missing and record.exists and self._needs_refresh(record):
+                self.refresh_async(record.path, source=record.source, location_id=record.location_id)
+        if changed:
+            self.save()
+        return self.sorted_records()
+
+    def sorted_records(self) -> list[DatasetRecord]:
+        records = list(self.datasets.values())
+        records.sort(
+            key=lambda item: (
+                not item.favorite,
+                not bool(item.last_used_at),
+                item.last_used_at or item.discovered_at,
+                item.name.casefold(),
+            ),
+            reverse=False,
+        )
+        favorites = sorted([item for item in records if item.favorite], key=lambda item: item.name.casefold())
+        recent = sorted(
+            [item for item in records if not item.favorite and item.last_used_at],
+            key=lambda item: item.last_used_at,
+            reverse=True,
+        )
+        others = sorted(
+            [item for item in records if not item.favorite and not item.last_used_at],
+            key=lambda item: item.discovered_at,
+            reverse=True,
+        )
+        return [*favorites, *recent, *others]
+
+    def record_for_path(self, path: str | Path) -> DatasetRecord:
+        key = _path_key(path)
+        record = self.datasets.get(key)
+        if record is None:
+            record = self._cached_or_sampled(Path(path), source="asset", location_id="")
+            self.datasets[key] = record
+            self.save()
+        return record
+
+    def favorite(self, path: str | Path, enabled: bool) -> DatasetRecord:
+        record = self.record_for_path(path)
+        record.favorite = bool(enabled)
+        self.save()
+        return record
+
+    def touch(self, path: str | Path) -> DatasetRecord:
+        record = self.record_for_path(path)
+        record.last_used_at = _now()
+        self.save()
+        return record
+
+    def refresh_async(self, path: str | Path, *, source: str = "asset", location_id: str = "") -> None:
+        resolved = str(Path(path).expanduser().resolve())
+        key = _path_key(resolved)
+        with _scan_lock:
+            if key in _active_scans:
+                return
+            _active_scans.add(key)
+
+        def worker() -> None:
+            try:
+                record = self._scan(Path(resolved), source=source, location_id=location_id, limit=SCAN_LIMIT)
+                fresh = DatasetRegistry(self.root, self.config)
+                current = fresh.datasets.get(record.id)
+                if current:
+                    record.favorite = current.favorite
+                    record.last_used_at = current.last_used_at
+                    record.discovered_at = current.discovered_at
+                fresh.datasets[record.id] = record
+                fresh.save()
+            finally:
+                with _scan_lock:
+                    _active_scans.discard(key)
+
+        threading.Thread(target=worker, name="ADAMDatasetScan", daemon=True).start()
+
+    def _automatic_location_candidates(self) -> list[tuple[Path, str, str]]:
+        candidates: list[tuple[Path, str, str]] = [(self.root / "ADAM_Datasets", "ADAM Datasets", "adam")]
+        folders = self.config.get("tool_folders", {}) if self.config is not None else {}
+        folders = folders if isinstance(folders, dict) else {}
+        collector_raw = str(folders.get("dataset_collector", "")).strip()
+        if collector_raw:
+            candidates.append((Path(collector_raw) / "Datasets", "Dataset Collector", "tool"))
+        for tool_id in ("ddpm_trainer", "lora_trainer", "flow_trainer", "oasis_trainer"):
+            raw_root = str(folders.get(tool_id, "")).strip()
+            if not raw_root:
+                continue
+            root = Path(raw_root)
+            label = tool_id.removesuffix("_trainer").upper()
+            for child in ("Datasets", "datasets", "OldDatasets"):
+                candidates.append((root / child, f"{label} {child}", "tool"))
+        external_tools = self.root / "config" / "external_tools.json"
+        try:
+            payload = json.loads(external_tools.read_text(encoding="utf-8"))
+        except (OSError, ValueError, TypeError, json.JSONDecodeError):
+            payload = {}
+        for entry in payload.get("tools", []) if isinstance(payload, dict) else []:
+            if not isinstance(entry, dict):
+                continue
+            backend = entry.get("backend", {})
+            if not isinstance(backend, dict):
+                continue
+            raw_root = str(backend.get("root", "")).strip()
+            if raw_root:
+                root = Path(raw_root)
+                candidates.append((root / "Datasets", f"{entry.get('name', 'External tool')} Datasets", "tool"))
+                candidates.append((root / "OldDatasets", f"{entry.get('name', 'External tool')} OldDatasets", "tool"))
+        return candidates
+
+    def _dataset_candidates(self, location: Path) -> list[Path]:
+        candidates: list[Path] = []
+        try:
+            children = [item for item in location.iterdir() if item.is_dir()]
+        except OSError:
+            children = []
+        for child in children[:1000]:
+            if self._looks_like_dataset(child):
+                candidates.append(child)
+        if candidates:
+            return candidates
+        if self._looks_like_dataset(location):
+            candidates.append(location)
+        return candidates
+
+    def _looks_like_dataset(self, folder: Path) -> bool:
+        if not folder.is_dir():
+            return False
+        try:
+            names = {item.name for item in folder.iterdir()}
+        except OSError:
+            return False
+        if names & DATASET_MARKERS:
+            return True
+        checked = 0
+        for item in folder.rglob("*"):
+            if checked >= 200:
+                break
+            checked += 1
+            if item.is_file() and item.suffix.casefold() in IMAGE_EXTENSIONS | VIDEO_EXTENSIONS:
+                return True
+        return False
+
+    def _cached_or_sampled(self, path: Path, *, source: str, location_id: str) -> DatasetRecord:
+        key = _path_key(path)
+        current = self.datasets.get(key)
+        if current is not None:
+            current.exists = path.is_dir()
+            current.source = current.source or source
+            current.location_id = current.location_id or location_id
+            return current
+        return self._scan(path, source=source, location_id=location_id, limit=800)
+
+    def _needs_refresh(self, record: DatasetRecord) -> bool:
+        if not record.scanned_at:
+            return True
+        scanned = _date(record.scanned_at)
+        return scanned is None or datetime.now(timezone.utc) - scanned > ASYNC_REFRESH_AFTER
+
+    def _scan(self, path: Path, *, source: str, location_id: str, limit: int) -> DatasetRecord:
+        resolved = path.expanduser().resolve()
+        record = DatasetRecord(
+            id=_path_key(resolved),
+            name=resolved.name or "Dataset",
+            path=str(resolved),
+            source=source,
+            location_id=location_id,
+            exists=resolved.is_dir(),
+            scanned_at=_now(),
+        )
+        if not resolved.is_dir():
+            record.warnings.append("Dataset folder is unavailable.")
+            return record
+        files_seen = 0
+        capped = False
+        try:
+            iterator = resolved.rglob("*")
+            for item in iterator:
+                if not item.is_file():
+                    continue
+                files_seen += 1
+                suffix = item.suffix.casefold()
+                if suffix in IMAGE_EXTENSIONS:
+                    record.image_count += 1
+                    if not record.sample_image:
+                        record.sample_image = str(item)
+                    if not any(item.with_suffix(ext).is_file() for ext in (".txt", ".caption")):
+                        record.missing_caption_count += 1
+                elif suffix in VIDEO_EXTENSIONS:
+                    record.video_count += 1
+                if suffix in TEXT_EXTENSIONS and suffix in {".txt", ".caption"}:
+                    record.caption_count += 1
+                if files_seen >= limit:
+                    capped = True
+                    break
+        except OSError as exc:
+            record.warnings.append(f"Dataset could not be scanned: {exc}")
+        record.item_count = record.image_count + record.video_count
+        record.dataset_format = self._format_label(resolved, record)
+        if capped:
+            record.warnings.append(f"Counts are sampled from the first {limit:,} files.")
+        return record
+
+    @staticmethod
+    def _format_label(folder: Path, record: DatasetRecord) -> str:
+        if (folder / "actions.jsonl").is_file() or (folder / "actions.csv").is_file():
+            return "Oasis action dataset"
+        if record.video_count:
+            return "Video dataset"
+        if record.image_count and record.caption_count:
+            return "Captioned image dataset"
+        if record.image_count:
+            return "Image dataset"
+        return "Dataset folder"

adam/eve.py

@@ -87,8 +87,9 @@ def classify_eve_embeddings(
 class EveVisionModel:
     """Lazy local DINOv2 feature extractor used by EVE."""
 
-    def __init__(self, model_id: str = EVE_MODEL_ID) -> None:
+    def __init__(self, model_id: str = EVE_MODEL_ID, *, prefer_gpu: bool = True) -> None:
         self.model_id = model_id
+        self.prefer_gpu = prefer_gpu
         self._processor = None
         self._model = None
         self._device = "cpu"
@@ -106,10 +107,20 @@ class EveVisionModel:
             raise RuntimeError(
                 "EVE needs PyTorch and Transformers. Launch ADAM with its normal Python environment."
             ) from exc
-        self._device = "cuda" if torch.cuda.is_available() else "cpu"
+        self._device = "cuda" if self.prefer_gpu and torch.cuda.is_available() else "cpu"
         self._processor = AutoImageProcessor.from_pretrained(self.model_id, use_fast=True)
         self._model = AutoModel.from_pretrained(self.model_id).to(self._device).eval()
 
+    def unload(self) -> None:
+        self._processor = None
+        self._model = None
+        try:
+            import torch
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        except ImportError:
+            pass
+
     def embed(self, paths: Sequence[str | Path], progress: Callable[[int, int], None] | None = None) -> list[list[float]]:
         self.load()
         import torch

adam/executor.py

@@ -12,6 +12,7 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Callable
 
+from adam.process_control import terminate_process_tree
 from adam.registry import ToolRegistry, ToolSpec
 
 
@@ -23,7 +24,15 @@ class ToolCancelled(ToolExecutionError):
     pass
 
 
-ProgressCallback = Callable[[int, str], None]
+class ToolAdjustmentRequested(ToolExecutionError):
+    """A trainer stopped cleanly so a job can continue with new settings."""
+
+    def __init__(self, message: str, details: dict[str, Any] | None = None) -> None:
+        super().__init__(message)
+        self.details = details or {}
+
+
+ProgressCallback = Callable[..., None]
 LogCallback = Callable[[str], None]
 PreviewCallback = Callable[[dict[str, Any]], None]
 
@@ -38,14 +47,16 @@ class ToolContext:
     progress_callback: ProgressCallback
     log_callback: LogCallback
     preview_callback: PreviewCallback = lambda _preview: None
+    adjustment_event: threading.Event | None = None
+    adjustment_request: dict[str, Any] | None = None
     step_delay: float = 0.2
 
     def log(self, message: str) -> None:
         self.log_callback(message)
 
-    def progress(self, percent: int, message: str) -> None:
+    def progress(self, percent: int, message: str, **details: Any) -> None:
         self.checkpoint()
-        self.progress_callback(max(0, min(int(percent), 100)), message)
+        self.progress_callback(max(0, min(int(percent), 100)), message, **details)
 
     def preview(
         self, path: str | Path, *, epoch: int = 0, next_epoch: int = 0,
@@ -104,6 +115,8 @@ class ToolExecutor:
         progress_callback: ProgressCallback,
         log_callback: LogCallback,
         preview_callback: PreviewCallback | None = None,
+        adjustment_event: threading.Event | None = None,
+        adjustment_request: dict[str, Any] | None = None,
     ) -> dict[str, Any]:
         spec = self.registry.get(tool_id)
         self._validate_arguments(spec, arguments)
@@ -116,6 +129,8 @@ class ToolExecutor:
             progress_callback=progress_callback,
             log_callback=log_callback,
             preview_callback=preview_callback or (lambda _preview: None),
+            adjustment_event=adjustment_event,
+            adjustment_request=adjustment_request,
             step_delay=self.step_delay,
         )
         backend_type = str(spec.backend.get("type", "")).lower()
@@ -243,30 +258,7 @@ class ToolExecutor:
 
         def stop_process_tree() -> None:
             """Stop the script and any workers it launched."""
-            if process_controller is not None:
-                try:
-                    descendants = process_controller.children(recursive=True)
-                    for child in descendants:
-                        try:
-                            child.terminate()
-                        except Exception:
-                            pass
-                    process_controller.terminate()
-                    try:
-                        import psutil
-
-                        _gone, alive = psutil.wait_procs(descendants, timeout=2)
-                        for child in alive:
-                            try:
-                                child.kill()
-                            except Exception:
-                                pass
-                    except Exception:
-                        pass
-                    return
-                except Exception:
-                    pass
-            process.terminate()
+            terminate_process_tree(process, timeout=3)
 
         try:
             while True:

adam/experiment_tracker.py

@@ -0,0 +1,387 @@
+from __future__ import annotations
+
+import json
+import re
+import sqlite3
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from adam.models import Job, JobStatus, SystemSnapshot
+
+
+def _utc_now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _json(value: Any) -> str:
+    return json.dumps(value, sort_keys=True)
+
+
+def _safe_json(value: str, fallback: Any) -> Any:
+    try:
+        parsed = json.loads(value or "")
+    except (TypeError, ValueError, json.JSONDecodeError):
+        return fallback
+    return parsed if isinstance(parsed, type(fallback)) else fallback
+
+
+def _safe_int(value: Any, default: int = 0) -> int:
+    try:
+        if isinstance(value, bool):
+            return default
+        return int(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _safe_float(value: Any, default: float = 0.0) -> float:
+    try:
+        if isinstance(value, bool):
+            return default
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _loss_from_logs(logs: list[str]) -> float | None:
+    for line in reversed(logs):
+        match = re.search(r"\bloss(?:\s*[:=]\s*|\s+)(-?\d+(?:\.\d+)?(?:e[+-]?\d+)?)", line, re.I)
+        if match:
+            try:
+                return float(match.group(1))
+            except ValueError:
+                return None
+    return None
+
+
+def _duration_seconds(job: Job) -> int:
+    if not job.started_at:
+        return 0
+    try:
+        start = datetime.fromisoformat(job.started_at)
+        end = datetime.fromisoformat(job.ended_at) if job.ended_at else datetime.now(timezone.utc)
+        return max(0, int((end - start).total_seconds()))
+    except ValueError:
+        return 0
+
+
+def _image_count(path: str) -> int:
+    folder = Path(path).expanduser()
+    if not folder.is_dir():
+        return 0
+    try:
+        return sum(
+            1 for item in folder.rglob("*")
+            if item.is_file() and item.suffix.casefold() in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+        )
+    except OSError:
+        return 0
+
+
+@dataclass(slots=True)
+class ExperimentRun:
+    id: str
+    job_id: str
+    timestamp: str
+    model_architecture: str
+    model_name: str
+    trigger_word: str
+    base_model: str
+    dataset_path: str
+    dataset_name: str
+    dataset_item_count: int
+    epochs: int
+    batch_size: int
+    learning_rate: float
+    optimizer: str
+    scheduler: str
+    resolution: int
+    seed: int
+    status: str
+    training_time_seconds: int
+    final_loss: float | None
+    output_folder: str
+    checkpoint_paths: list[str]
+    preview_images: list[str]
+    peak_vram_gb: float | None
+    hardware: dict[str, Any]
+    settings: dict[str, Any]
+    generation_settings: dict[str, Any]
+    notes: str = ""
+    quality_score: int | None = None
+
+    @classmethod
+    def from_row(cls, row: sqlite3.Row) -> "ExperimentRun":
+        payload = dict(row)
+        for key in ("checkpoint_paths", "preview_images"):
+            payload[key] = _safe_json(payload.get(key, "[]"), [])
+        for key in ("hardware", "settings", "generation_settings"):
+            payload[key] = _safe_json(payload.get(key, "{}"), {})
+        payload["quality_score"] = (
+            _safe_int(payload["quality_score"]) if payload.get("quality_score") is not None else None
+        )
+        return cls(**payload)
+
+
+class ExperimentStore:
+    def __init__(self, root: Path) -> None:
+        self.root = root.resolve()
+        self.path = self.root / "data" / "experiments.sqlite3"
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        self._init_db()
+
+    def connect(self) -> sqlite3.Connection:
+        connection = sqlite3.connect(self.path)
+        connection.row_factory = sqlite3.Row
+        return connection
+
+    def _init_db(self) -> None:
+        with self.connect() as db:
+            db.execute(
+                """
+                CREATE TABLE IF NOT EXISTS experiments (
+                    id TEXT PRIMARY KEY,
+                    job_id TEXT UNIQUE NOT NULL,
+                    timestamp TEXT NOT NULL,
+                    model_architecture TEXT NOT NULL,
+                    model_name TEXT NOT NULL,
+                    trigger_word TEXT NOT NULL DEFAULT '',
+                    base_model TEXT NOT NULL,
+                    dataset_path TEXT NOT NULL,
+                    dataset_name TEXT NOT NULL,
+                    dataset_item_count INTEGER NOT NULL,
+                    epochs INTEGER NOT NULL,
+                    batch_size INTEGER NOT NULL,
+                    learning_rate REAL NOT NULL,
+                    optimizer TEXT NOT NULL,
+                    scheduler TEXT NOT NULL,
+                    resolution INTEGER NOT NULL,
+                    seed INTEGER NOT NULL,
+                    status TEXT NOT NULL,
+                    training_time_seconds INTEGER NOT NULL,
+                    final_loss REAL,
+                    output_folder TEXT NOT NULL,
+                    checkpoint_paths TEXT NOT NULL,
+                    preview_images TEXT NOT NULL,
+                    peak_vram_gb REAL,
+                    hardware TEXT NOT NULL,
+                    settings TEXT NOT NULL,
+                    generation_settings TEXT NOT NULL,
+                    notes TEXT NOT NULL DEFAULT '',
+                    quality_score INTEGER
+                )
+                """
+            )
+            self._migrate_columns(db)
+
+    @staticmethod
+    def _migrate_columns(db: sqlite3.Connection) -> None:
+        existing = {row["name"] for row in db.execute("PRAGMA table_info(experiments)").fetchall()}
+        columns = {
+            "id": "TEXT PRIMARY KEY",
+            "job_id": "TEXT NOT NULL DEFAULT ''",
+            "timestamp": "TEXT NOT NULL DEFAULT ''",
+            "model_architecture": "TEXT NOT NULL DEFAULT ''",
+            "model_name": "TEXT NOT NULL DEFAULT ''",
+            "trigger_word": "TEXT NOT NULL DEFAULT ''",
+            "base_model": "TEXT NOT NULL DEFAULT ''",
+            "dataset_path": "TEXT NOT NULL DEFAULT ''",
+            "dataset_name": "TEXT NOT NULL DEFAULT ''",
+            "dataset_item_count": "INTEGER NOT NULL DEFAULT 0",
+            "epochs": "INTEGER NOT NULL DEFAULT 0",
+            "batch_size": "INTEGER NOT NULL DEFAULT 0",
+            "learning_rate": "REAL NOT NULL DEFAULT 0",
+            "optimizer": "TEXT NOT NULL DEFAULT ''",
+            "scheduler": "TEXT NOT NULL DEFAULT ''",
+            "resolution": "INTEGER NOT NULL DEFAULT 0",
+            "seed": "INTEGER NOT NULL DEFAULT 0",
+            "status": "TEXT NOT NULL DEFAULT ''",
+            "training_time_seconds": "INTEGER NOT NULL DEFAULT 0",
+            "final_loss": "REAL",
+            "output_folder": "TEXT NOT NULL DEFAULT ''",
+            "checkpoint_paths": "TEXT NOT NULL DEFAULT '[]'",
+            "preview_images": "TEXT NOT NULL DEFAULT '[]'",
+            "peak_vram_gb": "REAL",
+            "hardware": "TEXT NOT NULL DEFAULT '{}'",
+            "settings": "TEXT NOT NULL DEFAULT '{}'",
+            "generation_settings": "TEXT NOT NULL DEFAULT '{}'",
+            "notes": "TEXT NOT NULL DEFAULT ''",
+            "quality_score": "INTEGER",
+        }
+        for name, definition in columns.items():
+            if name not in existing and name != "id":
+                db.execute(f"ALTER TABLE experiments ADD COLUMN {name} {definition}")
+
+    def record_job(self, job: Job, snapshot: SystemSnapshot | None = None) -> ExperimentRun | None:
+        training_steps = [step for step in job.plan.steps if step.tool_id.endswith("_trainer")]
+        if not training_steps:
+            return None
+        step = training_steps[-1]
+        args = dict(step.arguments)
+        architecture = step.tool_id.removesuffix("_trainer")
+        dataset_path = str(args.get("dataset_dir", ""))
+        output_folder = str(job.output_folder or args.get("output_dir", ""))
+        preview_images = [job.preview_path] if job.preview_path else []
+        checkpoints = []
+        if output_folder:
+            folder = Path(output_folder)
+            if folder.is_dir():
+                try:
+                    checkpoints = [
+                        str(path)
+                        for path in sorted(folder.rglob("*"))
+                        if path.is_file() and path.suffix.casefold() in {".safetensors", ".ckpt", ".pt", ".bin"}
+                    ][-10:]
+                    discovered_previews = [
+                        str(path)
+                        for path in sorted(folder.rglob("*"))
+                        if path.is_file()
+                        and path.suffix.casefold() in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
+                        and any(token in path.name.casefold() for token in ("preview", "sample", "epoch"))
+                    ][-12:]
+                    preview_images = list(dict.fromkeys([*preview_images, *discovered_previews]))
+                except OSError:
+                    checkpoints = []
+        hardware = {}
+        peak_vram = None
+        if snapshot is not None:
+            hardware = {
+                "gpu_name": snapshot.gpu_name,
+                "gpu_percent": snapshot.gpu_percent,
+                "vram_used_gb": snapshot.vram_used_gb,
+                "vram_total_gb": snapshot.vram_total_gb,
+                "memory_used_gb": snapshot.memory_used_gb,
+                "memory_total_gb": snapshot.memory_total_gb,
+                "cpu_percent": snapshot.cpu_percent,
+                "gpu_temperature": snapshot.gpu_temperature,
+            }
+            peak_vram = snapshot.vram_used_gb or None
+        run = ExperimentRun(
+            id=f"EXP-{job.id}",
+            job_id=job.id,
+            timestamp=job.ended_at or _utc_now(),
+            model_architecture=architecture,
+            model_name=str(args.get("model_name", job.plan.project_name)),
+            trigger_word=str(args.get("trigger_word", "")),
+            base_model=str(args.get("base_model", args.get("base_model_path", ""))),
+            dataset_path=dataset_path,
+            dataset_name=Path(dataset_path).name if dataset_path else "",
+            dataset_item_count=_image_count(dataset_path),
+            epochs=_safe_int(args.get("epochs")),
+            batch_size=_safe_int(args.get("batch_size")),
+            learning_rate=_safe_float(args.get("learning_rate")),
+            optimizer=str(args.get("optimizer", "")),
+            scheduler=str(args.get("scheduler", args.get("sampler", ""))),
+            resolution=_safe_int(args.get("resolution")),
+            seed=_safe_int(args.get("seed", args.get("preview_seed", 0))),
+            status=job.status.value,
+            training_time_seconds=_duration_seconds(job),
+            final_loss=_loss_from_logs(job.logs),
+            output_folder=output_folder,
+            checkpoint_paths=checkpoints,
+            preview_images=preview_images,
+            peak_vram_gb=peak_vram,
+            hardware=hardware,
+            settings=args,
+            generation_settings={},
+        )
+        with self.connect() as db:
+            db.execute(
+                """
+                INSERT INTO experiments (
+                    id, job_id, timestamp, model_architecture, model_name, trigger_word, base_model,
+                    dataset_path, dataset_name, dataset_item_count, epochs, batch_size,
+                    learning_rate, optimizer, scheduler, resolution, seed, status,
+                    training_time_seconds, final_loss, output_folder, checkpoint_paths,
+                    preview_images, peak_vram_gb, hardware, settings, generation_settings
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                ON CONFLICT(job_id) DO UPDATE SET
+                    timestamp=excluded.timestamp,
+                    trigger_word=excluded.trigger_word,
+                    status=excluded.status,
+                    training_time_seconds=excluded.training_time_seconds,
+                    final_loss=excluded.final_loss,
+                    output_folder=excluded.output_folder,
+                    checkpoint_paths=excluded.checkpoint_paths,
+                    preview_images=excluded.preview_images,
+                    peak_vram_gb=excluded.peak_vram_gb,
+                    hardware=excluded.hardware,
+                    settings=excluded.settings
+                """,
+                (
+                    run.id, run.job_id, run.timestamp, run.model_architecture, run.model_name,
+                    run.trigger_word, run.base_model, run.dataset_path, run.dataset_name, run.dataset_item_count,
+                    run.epochs, run.batch_size, run.learning_rate, run.optimizer, run.scheduler,
+                    run.resolution, run.seed, run.status, run.training_time_seconds, run.final_loss,
+                    run.output_folder, _json(run.checkpoint_paths), _json(run.preview_images),
+                    run.peak_vram_gb, _json(run.hardware), _json(run.settings),
+                    _json(run.generation_settings),
+                ),
+            )
+        return run
+
+    def list_runs(self, search: str = "", architecture: str = "", dataset: str = "", limit: int = 200) -> list[ExperimentRun]:
+        clauses = []
+        params: list[Any] = []
+        if search:
+            clauses.append("(id LIKE ? OR job_id LIKE ? OR model_name LIKE ? OR dataset_name LIKE ? OR dataset_path LIKE ? OR output_folder LIKE ? OR notes LIKE ? OR status LIKE ?)")
+            term = f"%{search}%"
+            params.extend([term, term, term, term, term, term, term, term])
+        if architecture:
+            clauses.append("model_architecture = ?")
+            params.append(architecture)
+        if dataset:
+            clauses.append("dataset_name LIKE ?")
+            params.append(f"%{dataset}%")
+        where = " WHERE " + " AND ".join(clauses) if clauses else ""
+        with self.connect() as db:
+            rows = db.execute(
+                "SELECT * FROM experiments" + where + " ORDER BY timestamp DESC LIMIT ?",
+                [*params, int(limit)],
+            ).fetchall()
+        return [ExperimentRun.from_row(row) for row in rows]
+
+    def get(self, run_id: str) -> ExperimentRun | None:
+        with self.connect() as db:
+            row = db.execute("SELECT * FROM experiments WHERE id = ?", (run_id,)).fetchone()
+        return ExperimentRun.from_row(row) if row else None
+
+    def update_notes(self, run_id: str, notes: str, quality_score: int | None) -> None:
+        with self.connect() as db:
+            db.execute(
+                "UPDATE experiments SET notes = ?, quality_score = ? WHERE id = ?",
+                (notes, quality_score, run_id),
+            )
+
+    def compare(self, run_ids: list[str]) -> list[dict[str, Any]]:
+        runs = [run for run_id in run_ids if (run := self.get(run_id)) is not None]
+        fields = [
+            "model_architecture", "epochs", "final_loss", "training_time_seconds",
+            "resolution", "batch_size", "learning_rate", "scheduler",
+            "peak_vram_gb", "dataset_name", "quality_score",
+        ]
+        rows = []
+        for field in fields:
+            values = {run.id: getattr(run, field) for run in runs}
+            comparable = {str(value) for value in values.values()}
+            rows.append({"field": field, "changed": len(comparable) > 1, **values})
+        return rows
+
+    def clone_request(self, run_id: str) -> str:
+        run = self.get(run_id)
+        if run is None:
+            return ""
+        options = {
+            key: value
+            for key, value in run.settings.items()
+            if key not in {"dataset_dir", "model_name", "epochs", "output_dir", "resume_from"}
+        }
+        return (
+            f"From the {run.dataset_name or run.dataset_path} dataset, train a "
+            f"{run.model_architecture.upper()} model for {run.epochs} epochs. "
+            f"Name the model {run.model_name} Clone. "
+            "[ADAM_TRAINING_OPTIONS:" + json.dumps(options, sort_keys=True) + "] "
+            "[ADAM_TRAINER:" + run.model_architecture + "]"
+        )

adam/generations.py

@@ -219,6 +219,7 @@ def generation_metadata_path(folder: Path, timestamp: str, job_id: str) -> Path:
 
 @dataclass(frozen=True, slots=True)
 class GenerationRecord:
+    metadata_path: Path
     folder: Path
     images: tuple[Path, ...]
     provider_id: str
@@ -231,6 +232,8 @@ class GenerationRecord:
     sampler: str
     aspect_ratio: str
     created_at: str
+    smart_generation: dict[str, Any]
+    image_evaluations: dict[str, dict[str, Any]]
 
     @classmethod
     def from_metadata(cls, metadata_path: Path) -> "GenerationRecord | None":
@@ -246,6 +249,7 @@ class GenerationRecord:
         if not images:
             return None
         return cls(
+            metadata_path=metadata_path,
             folder=folder,
             images=images,
             provider_id=str(payload.get("provider_id", "")),
@@ -258,9 +262,77 @@ class GenerationRecord:
             sampler=str(payload.get("sampler", "")),
             aspect_ratio=str(payload.get("aspect_ratio", "")),
             created_at=str(payload.get("created_at", "")),
+            smart_generation=dict(payload.get("smart_generation") or {}),
+            image_evaluations={
+                str(Path(path).resolve()): dict(value)
+                for path, value in dict(payload.get("image_evaluations") or {}).items()
+                if isinstance(value, dict)
+            },
         )
 
 
+@dataclass(frozen=True, slots=True)
+class GenerationModelFolder:
+    """A model-centered view over existing generation batches."""
+
+    key: str
+    model_name: str
+    model_path: str
+    provider_id: str
+    provider_name: str
+    records: tuple[GenerationRecord, ...]
+    image_count: int
+    cover_image: Path | None
+    latest_at: str
+
+
+def generation_model_key(record: GenerationRecord) -> str:
+    """Keep renamed or duplicated display names separated by model identity."""
+    raw_path = str(record.model_path or "").strip()
+    if raw_path:
+        try:
+            return f"path:{Path(raw_path).expanduser().resolve()}".casefold()
+        except OSError:
+            return f"path:{raw_path}".casefold()
+    return f"name:{record.provider_id}:{record.model_name}".casefold()
+
+
+def group_generation_records(
+    records: list[GenerationRecord],
+) -> list[GenerationModelFolder]:
+    """Build newest-first automatic model folders without changing files."""
+    grouped: dict[str, list[GenerationRecord]] = {}
+    for record in records:
+        grouped.setdefault(generation_model_key(record), []).append(record)
+    folders: list[GenerationModelFolder] = []
+    for key, model_records in grouped.items():
+        newest_first = sorted(
+            model_records,
+            key=lambda item: item.created_at or item.folder.name,
+            reverse=True,
+        )
+        latest = newest_first[0]
+        cover = next(
+            (path for record in newest_first for path in record.images if path.is_file()),
+            None,
+        )
+        folders.append(
+            GenerationModelFolder(
+                key=key,
+                model_name=latest.model_name,
+                model_path=latest.model_path,
+                provider_id=latest.provider_id,
+                provider_name=latest.provider_name,
+                records=tuple(newest_first),
+                image_count=sum(len(record.images) for record in newest_first),
+                cover_image=cover,
+                latest_at=latest.created_at,
+            )
+        )
+    folders.sort(key=lambda item: (item.latest_at, item.model_name.casefold()), reverse=True)
+    return folders
+
+
 def generation_tools(registry: ToolRegistry) -> list[ToolSpec]:
     return [
         tool

adam/image_preferences.py

@@ -0,0 +1,313 @@
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timezone
+import hashlib
+import json
+from pathlib import Path
+from typing import Any, Callable, Sequence
+
+from adam.eve import EveVisionModel, classify_eve_embeddings
+
+
+RATINGS = {"favorite", "keep", "unsure", "reject"}
+POSITIVE_RATINGS = {"favorite", "keep"}
+NEGATIVE_RATINGS = {"reject"}
+
+
+def _now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def preference_profile_id(provider_id: str, model_path: str) -> str:
+    key = f"{provider_id}\n{str(Path(model_path).expanduser().resolve())}"
+    return hashlib.sha1(key.encode("utf-8")).hexdigest()[:16]
+
+
+def image_cache_id(path: str | Path) -> str:
+    resolved = str(Path(path).expanduser().resolve())
+    return hashlib.sha1(resolved.encode("utf-8")).hexdigest()
+
+
+@dataclass(slots=True)
+class GenerationRating:
+    image_path: str
+    rating: str
+    provider_id: str
+    model_name: str
+    model_path: str
+    seed: int = 0
+    sampler: str = ""
+    steps: int = 0
+    resolution: str = ""
+    generation_settings: dict[str, Any] = field(default_factory=dict)
+    generation_created_at: str = ""
+    rated_at: str = field(default_factory=_now)
+    embedding: list[float] | None = None
+
+    @classmethod
+    def from_dict(cls, payload: dict[str, Any]) -> "GenerationRating":
+        rating = str(payload.get("rating", "unsure")).casefold()
+        return cls(
+            image_path=str(Path(str(payload.get("image_path", ""))).expanduser().resolve()),
+            rating=rating if rating in RATINGS else "unsure",
+            provider_id=str(payload.get("provider_id", "")),
+            model_name=str(payload.get("model_name", "")),
+            model_path=str(payload.get("model_path", "")),
+            seed=int(payload.get("seed", 0) or 0),
+            sampler=str(payload.get("sampler", "")),
+            steps=int(payload.get("steps", 0) or 0),
+            resolution=str(payload.get("resolution", "")),
+            generation_settings=dict(payload.get("generation_settings") or {}),
+            generation_created_at=str(payload.get("generation_created_at", "")),
+            rated_at=str(payload.get("rated_at") or _now()),
+            embedding=[float(value) for value in payload["embedding"]]
+            if isinstance(payload.get("embedding"), list)
+            else None,
+        )
+
+
+@dataclass(frozen=True, slots=True)
+class PreferenceScore:
+    image_path: str
+    score: float | None
+    confidence: float
+    category: str
+    reason: str = ""
+
+
+class PreferenceProfile:
+    def __init__(self, root: Path, provider_id: str, model_name: str, model_path: str) -> None:
+        self.root = root.resolve()
+        self.provider_id = provider_id
+        self.model_name = model_name
+        self.model_path = str(Path(model_path).expanduser().resolve()) if model_path else ""
+        self.id = preference_profile_id(provider_id, self.model_path)
+        self.path = self.root / "data" / "generation_preferences" / f"{self.id}.json"
+        self.keep_threshold = 0.70
+        self.reject_threshold = 0.35
+        self.ratings: dict[str, GenerationRating] = {}
+        self.load()
+
+    def load(self) -> None:
+        try:
+            payload = json.loads(self.path.read_text(encoding="utf-8"))
+        except (OSError, ValueError, TypeError, json.JSONDecodeError):
+            return
+        self.model_name = str(payload.get("model_name") or self.model_name)
+        self.provider_id = str(payload.get("provider_id") or self.provider_id)
+        self.model_path = str(payload.get("model_path") or self.model_path)
+        thresholds = payload.get("thresholds", {})
+        if isinstance(thresholds, dict):
+            self.keep_threshold = float(thresholds.get("keep", self.keep_threshold))
+            self.reject_threshold = float(thresholds.get("reject", self.reject_threshold))
+        ratings = payload.get("ratings", [])
+        if isinstance(ratings, list):
+            for item in ratings:
+                if isinstance(item, dict):
+                    rating = GenerationRating.from_dict(item)
+                    self.ratings[rating.image_path] = rating
+
+    def save(self) -> None:
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        temporary = self.path.with_suffix(".tmp")
+        temporary.write_text(
+            json.dumps(
+                {
+                    "version": 1,
+                    "profile_id": self.id,
+                    "provider_id": self.provider_id,
+                    "model_name": self.model_name,
+                    "model_path": self.model_path,
+                    "thresholds": {
+                        "keep": self.keep_threshold,
+                        "reject": self.reject_threshold,
+                    },
+                    "ratings": [asdict(item) for item in self.ratings.values()],
+                    "updated_at": _now(),
+                },
+                indent=2,
+            ),
+            encoding="utf-8",
+        )
+        temporary.replace(self.path)
+
+    def set_rating(
+        self,
+        image_path: str | Path,
+        rating: str,
+        *,
+        seed: int = 0,
+        sampler: str = "",
+        steps: int = 0,
+        resolution: str = "",
+        generation_settings: dict[str, Any] | None = None,
+        generation_created_at: str = "",
+        embedding: Sequence[float] | None = None,
+    ) -> GenerationRating:
+        clean = rating.casefold().strip()
+        if clean not in RATINGS:
+            raise ValueError("Generation rating must be Favorite, Keep, Unsure, or Reject.")
+        resolved = str(Path(image_path).expanduser().resolve())
+        existing = self.ratings.get(resolved)
+        record = GenerationRating(
+            image_path=resolved,
+            rating=clean,
+            provider_id=self.provider_id,
+            model_name=self.model_name,
+            model_path=self.model_path,
+            seed=int(seed),
+            sampler=sampler,
+            steps=int(steps),
+            resolution=resolution,
+            generation_settings=dict(generation_settings or {}),
+            generation_created_at=generation_created_at,
+            rated_at=_now(),
+            embedding=[float(value) for value in embedding] if embedding is not None else (
+                existing.embedding if existing else None
+            ),
+        )
+        self.ratings[resolved] = record
+        self.save()
+        return record
+
+    def rating_for(self, image_path: str | Path) -> GenerationRating | None:
+        return self.ratings.get(str(Path(image_path).expanduser().resolve()))
+
+    def examples(self) -> tuple[list[GenerationRating], list[GenerationRating]]:
+        positive = [
+            item for item in self.ratings.values()
+            if item.rating in POSITIVE_RATINGS and Path(item.image_path).is_file()
+        ]
+        negative = [
+            item for item in self.ratings.values()
+            if item.rating in NEGATIVE_RATINGS and Path(item.image_path).is_file()
+        ]
+        return positive, negative
+
+    def has_signal(self) -> bool:
+        positive, _negative = self.examples()
+        return bool(positive)
+
+
+class ImageEmbeddingCache:
+    def __init__(self, root: Path, model_id: str) -> None:
+        self.root = root.resolve()
+        self.model_id = model_id
+        self.folder = self.root / "data" / "image_embeddings" / hashlib.sha1(model_id.encode("utf-8")).hexdigest()[:12]
+
+    def get(self, path: str | Path) -> list[float] | None:
+        cache_path = self.folder / f"{image_cache_id(path)}.json"
+        try:
+            payload = json.loads(cache_path.read_text(encoding="utf-8"))
+        except (OSError, ValueError, TypeError, json.JSONDecodeError):
+            return None
+        source = Path(path).expanduser().resolve()
+        try:
+            stat = source.stat()
+        except OSError:
+            return None
+        if payload.get("path") != str(source) or payload.get("mtime") != stat.st_mtime:
+            return None
+        vector = payload.get("embedding")
+        return [float(value) for value in vector] if isinstance(vector, list) else None
+
+    def set(self, path: str | Path, embedding: Sequence[float]) -> None:
+        source = Path(path).expanduser().resolve()
+        try:
+            stat = source.stat()
+        except OSError:
+            return
+        self.folder.mkdir(parents=True, exist_ok=True)
+        cache_path = self.folder / f"{image_cache_id(source)}.json"
+        temporary = cache_path.with_suffix(".tmp")
+        temporary.write_text(
+            json.dumps(
+                {
+                    "path": str(source),
+                    "mtime": stat.st_mtime,
+                    "model_id": self.model_id,
+                    "embedding": [float(value) for value in embedding],
+                }
+            ),
+            encoding="utf-8",
+        )
+        temporary.replace(cache_path)
+
+
+class GenerationPreferenceEvaluator:
+    """Shared EVE-backed scorer for generated images."""
+
+    def __init__(
+        self,
+        root: Path,
+        vision: EveVisionModel | None = None,
+        *,
+        embedder: Callable[[Sequence[str | Path]], list[list[float]]] | None = None,
+    ) -> None:
+        self.root = root.resolve()
+        self.vision = vision or EveVisionModel(prefer_gpu=False)
+        self.embedder = embedder
+        self.cache = ImageEmbeddingCache(self.root, self.vision.model_id)
+
+    def _embedding(self, path: str | Path) -> list[float]:
+        cached = self.cache.get(path)
+        if cached is not None:
+            return cached
+        vectors = self.embedder([path]) if self.embedder else self.vision.embed([path])
+        vector = [float(value) for value in vectors[0]]
+        self.cache.set(path, vector)
+        return vector
+
+    def score(
+        self,
+        profile: PreferenceProfile,
+        image_paths: Sequence[str | Path],
+        *,
+        keep_threshold: float | None = None,
+        reject_threshold: float | None = None,
+    ) -> list[PreferenceScore]:
+        positive, negative = profile.examples()
+        if not positive:
+            return [
+                PreferenceScore(str(Path(path).expanduser().resolve()), None, 0.0, "Needs Review", "No preference examples yet")
+                for path in image_paths
+            ]
+        positive_vectors = [item.embedding or self._embedding(item.image_path) for item in positive]
+        negative_vectors = [item.embedding or self._embedding(item.image_path) for item in negative]
+        image_vectors = [self._embedding(path) for path in image_paths]
+        keep = max(0.001, min(1.0, float(keep_threshold if keep_threshold is not None else profile.keep_threshold)))
+        reject = max(0.0, min(float(reject_threshold if reject_threshold is not None else profile.reject_threshold), keep - 0.001))
+        results = classify_eve_embeddings(
+            image_paths,
+            image_vectors,
+            positive_vectors,
+            negative_vectors,
+            keep_threshold=keep,
+            reject_threshold=reject,
+        )
+        categories = {"keep": "Strong Keep", "reject": "Likely Reject", "unreviewed": "Needs Review"}
+        return [
+            PreferenceScore(result.path, result.match_score, result.decision_confidence, categories[result.suggestion])
+            for result in results
+        ]
+
+
+def score_generated_images(
+    root: Path,
+    *,
+    provider_id: str,
+    model_name: str,
+    model_path: str,
+    image_paths: Sequence[str | Path],
+    keep_threshold: float | None = None,
+    reject_threshold: float | None = None,
+) -> list[PreferenceScore]:
+    profile = PreferenceProfile(root, provider_id, model_name, model_path)
+    evaluator = GenerationPreferenceEvaluator(root)
+    return evaluator.score(
+        profile,
+        image_paths,
+        keep_threshold=keep_threshold,
+        reject_threshold=reject_threshold,
+    )

adam/job_manager.py

@@ -5,17 +5,27 @@ import logging
 import math
 import re
 import threading
-from datetime import datetime, timezone
+import time
+from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Any
 
-from PySide6.QtCore import QObject, QThread, Signal
+from PySide6.QtCore import QCoreApplication, QObject, QThread, QTimer, Signal
 
-from adam.executor import ToolCancelled, ToolExecutionError, ToolExecutor
+from adam.executor import ToolAdjustmentRequested, ToolCancelled, ToolExecutionError, ToolExecutor
 from adam.assets import AssetRegistry
 from adam.atlas import AtlasSupervisor
+from adam.experiment_tracker import ExperimentStore
 from adam.models import ExecutionPlan, Job, JobStatus, StepStatus, utc_now
 from adam.nova import evaluate_job_output
+from adam.training_assistant import append_preflight_summary
+
+
+def _safe_int(value: Any) -> int:
+    try:
+        return max(0, int(value or 0))
+    except (TypeError, ValueError):
+        return 0
 
 
 class JobWorker(QThread):
@@ -28,6 +38,8 @@ class JobWorker(QThread):
         self.cancel_event = threading.Event()
         self.run_event = threading.Event()
         self.run_event.set()
+        self.adjustment_event = threading.Event()
+        self.adjustment_request: dict[str, Any] = {}
 
     def pause(self) -> None:
         self.run_event.clear()
@@ -39,6 +51,12 @@ class JobWorker(QThread):
         self.cancel_event.set()
         self.run_event.set()
 
+    def request_adjustment(self, updates: dict[str, Any]) -> None:
+        self.adjustment_request.clear()
+        self.adjustment_request.update(updates)
+        self.adjustment_event.set()
+        self.run_event.set()
+
     def run(self) -> None:
         total_steps = len(self.job.plan.steps)
         try:
@@ -54,15 +72,25 @@ class JobWorker(QThread):
                 )
 
                 preview_state = {"epoch": 0, "path": ""}
+                last_progress_emit = {"time": 0.0, "overall": -1, "message": ""}
+                progress_samples: list[dict[str, float]] = []
 
-                def on_progress(percent: int, message: str, step_index: int = index) -> None:
+                def on_progress(percent: int, message: str, step_index: int = index, **details: Any) -> None:
                     overall = int(((step_index + percent / 100) / total_steps) * 100)
+                    now = time.monotonic()
+                    progress_eta = self._estimate_step_eta(details, progress_samples, now)
+                    changed = overall != last_progress_emit["overall"] or message != last_progress_emit["message"]
+                    terminal = percent >= 100 or overall >= 100
+                    if not terminal and (not changed or now - last_progress_emit["time"] < 0.25):
+                        return
+                    last_progress_emit.update({"time": now, "overall": overall, "message": message})
                     self.event.emit(
                         {
                             "type": "progress",
                             "step_percent": percent,
                             "overall": overall,
                             "message": message,
+                            **progress_eta,
                         }
                     )
                     self._discover_external_preview(step, message, preview_state)
@@ -84,6 +112,8 @@ class JobWorker(QThread):
                     progress_callback=on_progress,
                     log_callback=on_log,
                     preview_callback=on_preview,
+                    adjustment_event=self.adjustment_event,
+                    adjustment_request=self.adjustment_request,
                 )
                 self.event.emit(
                     {
@@ -95,6 +125,8 @@ class JobWorker(QThread):
             self.event.emit({"type": "completed"})
         except ToolCancelled as exc:
             self.event.emit({"type": "cancelled", "message": str(exc)})
+        except ToolAdjustmentRequested as exc:
+            self.event.emit({"type": "adjustment_ready", "message": str(exc), **exc.details})
         except Exception as exc:
             self.event.emit(
                 {
@@ -140,6 +172,61 @@ class JobWorker(QThread):
                 "steps": int(step.arguments.get("preview_steps", 0) or 0),
             })
 
+    @staticmethod
+    def _estimate_step_eta(
+        details: dict[str, Any],
+        samples: list[dict[str, float]],
+        now: float,
+    ) -> dict[str, Any]:
+        """Estimate remaining runtime from real step cadence instead of percent alone."""
+        current = _safe_int(details.get("current_step", details.get("step", details.get("current"))))
+        total = _safe_int(details.get("total_steps", details.get("total")))
+        unit = str(details.get("unit", "step") or "step")
+        epoch = _safe_int(details.get("epoch"))
+        total_epochs = _safe_int(details.get("total_epochs"))
+        if (not current or not total) and epoch and total_epochs:
+            current, total, unit = epoch, total_epochs, "epoch"
+        payload: dict[str, Any] = {
+            "progress_current": current,
+            "progress_total": total,
+            "progress_unit": unit,
+        }
+        if not current or not total or current >= total:
+            return payload
+        last = samples[-1] if samples else None
+        if last and current <= last["current"]:
+            return payload
+        samples.append({"time": now, "current": float(current)})
+        del samples[:-25]
+        if len(samples) < 2:
+            return payload
+
+        first = samples[0]
+        elapsed = now - first["time"]
+        completed = current - int(first["current"])
+        if completed <= 0 or elapsed <= 0:
+            return payload
+        lifetime_seconds_per_unit = elapsed / completed
+
+        recent = samples[-8:]
+        recent_first = recent[0]
+        recent_completed = current - int(recent_first["current"])
+        recent_elapsed = now - recent_first["time"]
+        recent_seconds_per_unit = (
+            recent_elapsed / recent_completed
+            if recent_completed > 0 and recent_elapsed > 0
+            else lifetime_seconds_per_unit
+        )
+        seconds_per_unit = (recent_seconds_per_unit * 0.65) + (lifetime_seconds_per_unit * 0.35)
+        remaining = max(0, int(round((total - current) * seconds_per_unit)))
+        if remaining:
+            payload["eta_seconds"] = remaining
+            payload["progress_rate"] = 1 / seconds_per_unit if seconds_per_unit > 0 else 0.0
+            payload["estimated_completion_at"] = (
+                datetime.now(timezone.utc) + timedelta(seconds=remaining)
+            ).isoformat()
+        return payload
+
 
 class JobManager(QObject):
     job_created = Signal(object)
@@ -159,30 +246,57 @@ class JobManager(QObject):
         self.root = root
         self.executor = executor
         self.logger = logger
+        self.config = config if config is not None else {}
         self.jobs_path = root / "data" / "jobs.json"
         self.assets = AssetRegistry(root)
         self.atlas = AtlasSupervisor(config)
+        self.experiments = ExperimentStore(root)
         self.jobs: list[Job] = []
         self._queue: list[str] = []
         self._worker: JobWorker | None = None
         self._active_job: Job | None = None
+        self._last_snapshot = None
+        self._pending_update_job_ids: set[str] = set()
+        self._pending_update_timer_active = False
         self._load()
+        self._schedule_timer = QTimer(self)
+        self._schedule_timer.timeout.connect(self._release_due_scheduled)
+        if QCoreApplication.instance() is not None:
+            self._schedule_timer.start(15_000)
+            QTimer.singleShot(0, self._release_due_scheduled)
+            if self._queue:
+                QTimer.singleShot(0, self._start_next)
 
     @property
     def active_job(self) -> Job | None:
         return self._active_job
 
-    def submit(self, plan: ExecutionPlan) -> Job:
+    @property
+    def pending_jobs(self) -> list[Job]:
+        return [
+            job for job in self.jobs
+            if job.status in {JobStatus.AWAITING_CONFIRMATION, JobStatus.SCHEDULED, JobStatus.QUEUED}
+        ]
+
+    def submit(self, plan: ExecutionPlan, scheduled_for: str | None = None) -> Job:
+        # Every entry point must review a plan before its queue state is chosen.
+        # UI and Remote may prepare it earlier to keep filesystem work off Qt.
+        append_preflight_summary(plan, self.config)
+        is_future = self._is_future(scheduled_for)
         status = (
             JobStatus.AWAITING_CONFIRMATION
             if plan.requires_confirmation
-            else JobStatus.QUEUED
+            else JobStatus.SCHEDULED if is_future else JobStatus.QUEUED
         )
-        job = Job(plan=plan, status=status)
+        job = Job(plan=plan, status=status, scheduled_for=scheduled_for if is_future else None)
         self.jobs.insert(0, job)
         self._append_log(job, f"Plan created: {plan.summary}")
         if plan.requires_confirmation:
             self._append_log(job, "Waiting for user confirmation.")
+            if is_future:
+                self._append_log(job, f"Requested start time: {self._display_time(scheduled_for)}.")
+        elif is_future:
+            self._append_log(job, f"Scheduled for {self._display_time(scheduled_for)}.")
         else:
             self._queue.append(job.id)
         self._save()
@@ -196,9 +310,12 @@ class JobManager(QObject):
         job = self.get(job_id)
         if job.status != JobStatus.AWAITING_CONFIRMATION:
             return
-        job.status = JobStatus.QUEUED
+        job.status = JobStatus.SCHEDULED if self._is_future(job.scheduled_for) else JobStatus.QUEUED
         self._append_log(job, "Plan approved by user.")
-        self._queue.append(job.id)
+        if job.status == JobStatus.SCHEDULED:
+            self._append_log(job, f"Training will become eligible at {self._display_time(job.scheduled_for)}.")
+        else:
+            self._queue.append(job.id)
         self._save()
         self.job_updated.emit(job)
         self._start_next()
@@ -236,10 +353,13 @@ class JobManager(QObject):
         if job is self._active_job and self._worker:
             self._append_log(job, "Cancellation requested.")
             self._worker.cancel()
+            self._save()
+            self.job_updated.emit(job)
             return
         if job.id in self._queue:
             self._queue.remove(job.id)
         if job.status in {
+            JobStatus.SCHEDULED,
             JobStatus.QUEUED,
             JobStatus.AWAITING_CONFIRMATION,
             JobStatus.DRAFT,
@@ -250,6 +370,104 @@ class JobManager(QObject):
             self._save()
             self.job_updated.emit(job)
 
+    def request_training_adjustment(self, job_id: str, updates: dict[str, Any]) -> None:
+        """Apply safe DDPM settings after the current epoch and resume automatically."""
+        job = self.get(job_id)
+        if job is not self._active_job or job.status not in {JobStatus.RUNNING, JobStatus.PAUSED} or not self._worker:
+            raise ValueError("Only the active training job can be adjusted.")
+        if not (0 <= job.current_step < len(job.plan.steps)):
+            raise ValueError("The active training step is unavailable.")
+        step = job.plan.steps[job.current_step]
+        if step.tool_id != "ddpm_trainer":
+            raise ValueError("Safe epoch-boundary adjustment currently supports DDPM training.")
+        allowed = {"batch_size", "training_intensity", "gradient_accumulation_steps"}
+        cleaned = {key: int(value) for key, value in updates.items() if key in allowed}
+        if not cleaned or not 1 <= cleaned.get("batch_size", 1) <= 64 \
+                or not 10 <= cleaned.get("training_intensity", 100) <= 100 \
+                or not 1 <= cleaned.get("gradient_accumulation_steps", 1) <= 64:
+            raise ValueError("The requested training settings are outside ADAM's safe range.")
+        previous = {key: step.arguments.get(key) for key in cleaned}
+        if all(previous[key] == value for key, value in cleaned.items()):
+            raise ValueError("Those settings are already active.")
+        job.status = JobStatus.RUNNING
+        self._append_log(job, f"Adjustment queued for the end of this epoch: {cleaned}.")
+        self._worker.request_adjustment(cleaned)
+        self._save()
+        self.job_updated.emit(job)
+
+    def safer_vram_retry(self, job_id: str) -> Job:
+        """Create a checkpoint-aware DDPM retry with a smaller physical batch."""
+        original = self.get(job_id)
+        if original.status != JobStatus.FAILED or not self._looks_like_vram_failure(original):
+            raise ValueError("This job did not fail with a recognizable VRAM error.")
+        plan = ExecutionPlan.from_dict(original.to_dict()["plan"])
+        start_index = max(0, min(original.current_step, len(plan.steps) - 1))
+        plan.steps = plan.steps[start_index:]
+        step = plan.steps[0]
+        old_batch = max(1, int(step.arguments.get("batch_size", 1)))
+        if old_batch <= 1:
+            raise ValueError("Batch size is already 1; lower resolution or enable other memory-saving options.")
+        original_epochs = max(1, int(step.arguments.get("epochs", 1)))
+        resume_note = self._prepare_ddpm_resume(step.arguments, step.tool_id)
+        remaining_epochs = max(1, int(step.arguments.get("epochs", original_epochs)))
+        completed_epochs = max(0, original_epochs - remaining_epochs) if resume_note else 0
+        new_batch = max(1, old_batch // 2)
+        old_accumulation = max(1, int(step.arguments.get("gradient_accumulation_steps", 1)))
+        step.arguments["batch_size"] = new_batch
+        step.arguments["gradient_accumulation_steps"] = min(64, old_accumulation * max(1, math.ceil(old_batch / new_batch)))
+        if completed_epochs:
+            step.arguments["completed_epochs"] = completed_epochs
+        for item in plan.steps:
+            item.status = StepStatus.PENDING
+        plan.id = original.plan.id + "-vram-retry"
+        plan.created_at = utc_now()
+        plan.requires_confirmation = True
+        plan.confirmation_reason = "VRAM recovery reduced the physical batch and preserved the effective batch with gradient accumulation."
+        retry = self.submit(plan)
+        self._append_log(retry, f"VRAM recovery changed batch {old_batch} → {new_batch} and gradient accumulation {old_accumulation} → {step.arguments['gradient_accumulation_steps']}.")
+        if resume_note:
+            self._append_log(retry, resume_note)
+        return retry
+
+    @staticmethod
+    def _looks_like_vram_failure(job: Job) -> bool:
+        text = "\n".join([job.error or "", *job.logs[-100:]]).lower()
+        return any(token in text for token in ("out of memory", "cuda oom", "cuda error: out of memory"))
+
+    @staticmethod
+    def _is_future(value: str | None) -> bool:
+        if not value:
+            return False
+        try:
+            scheduled = datetime.fromisoformat(value)
+            if scheduled.tzinfo is None:
+                scheduled = scheduled.astimezone()
+            return scheduled.astimezone(timezone.utc) > datetime.now(timezone.utc)
+        except (TypeError, ValueError):
+            return False
+
+    @staticmethod
+    def _display_time(value: str | None) -> str:
+        try:
+            return datetime.fromisoformat(str(value)).astimezone().strftime("%b %d at %I:%M %p")
+        except ValueError:
+            return str(value or "the requested time")
+
+    def _release_due_scheduled(self) -> None:
+        released: list[Job] = []
+        for job in reversed(self.jobs):
+            if job.status == JobStatus.SCHEDULED and not self._is_future(job.scheduled_for):
+                job.status = JobStatus.QUEUED
+                self._queue.append(job.id)
+                self._append_log(job, "Scheduled start time reached; waiting for the training slot.")
+                released.append(job)
+        if not released:
+            return
+        self._save()
+        for job in released:
+            self.job_updated.emit(job)
+        self._start_next()
+
     def get(self, job_id: str) -> Job:
         for job in self.jobs:
             if job.id == job_id:
@@ -383,14 +601,30 @@ class JobManager(QObject):
                 job.preview_total = 0
                 job.preview_image_index = 0
                 job.preview_image_count = 0
+                job.eta_seconds = None
+                job.estimated_completion_at = None
+                job.progress_current = 0
+                job.progress_total = 0
+                job.progress_rate = 0.0
+                job.progress_unit = "step"
             self._append_log(job, f"Starting: {job.plan.steps[index].title}")
         elif event_type == "progress":
             job.progress = int(event["overall"])
+            job.eta_seconds = _safe_int(event.get("eta_seconds")) or None
+            job.estimated_completion_at = str(event.get("estimated_completion_at") or "") or None
+            job.progress_current = _safe_int(event.get("progress_current"))
+            job.progress_total = _safe_int(event.get("progress_total"))
+            job.progress_rate = float(event.get("progress_rate", 0.0) or 0.0)
+            job.progress_unit = str(event.get("progress_unit", "step") or "step")
             message = str(event["message"])
             if message and (not job.logs or message not in job.logs[-1]):
                 self._append_log(job, message)
+            self._schedule_job_update(job)
+            return
         elif event_type == "log":
             self._append_log(job, str(event["message"]))
+            self._schedule_job_update(job)
+            return
         elif event_type == "preview":
             job.preview_path = str(event.get("path", "")) or None
             job.preview_epoch = int(event.get("epoch", 0) or 0)
@@ -407,6 +641,8 @@ class JobManager(QObject):
             label = "Denoising" if job.preview_kind == "generation" else "Training"
             position = f" step {job.preview_current}" if job.preview_current else f" epoch {job.preview_epoch}"
             self._append_log(job, f"{label} preview updated at{position}.")
+            self._schedule_job_update(job)
+            return
         elif event_type == "step_finished":
             index = int(event["index"])
             job.plan.steps[index].status = StepStatus.FINISHED
@@ -432,8 +668,11 @@ class JobManager(QObject):
         elif event_type == "completed":
             job.status = JobStatus.FINISHED
             job.progress = 100
+            job.eta_seconds = 0
+            job.estimated_completion_at = utc_now()
             job.ended_at = utc_now()
             self._append_log(job, "Job finished successfully.")
+            self._record_experiment(job)
             self.notification.emit("Job complete", job.plan.project_name)
         elif event_type == "cancelled":
             job.status = JobStatus.CANCELLED
@@ -442,7 +681,31 @@ class JobManager(QObject):
             for step in job.plan.steps:
                 if step.status == StepStatus.RUNNING:
                     step.status = StepStatus.SKIPPED
+            self._record_experiment(job)
             self.notification.emit("Job cancelled", job.plan.project_name)
+        elif event_type == "adjustment_ready":
+            index = max(0, min(job.current_step, len(job.plan.steps) - 1))
+            remaining_steps = job.plan.steps[index:]
+            step = remaining_steps[0]
+            updates = dict(event.get("updates") or {})
+            step.arguments.update(updates)
+            checkpoint = str(event.get("checkpoint", ""))
+            completed_epochs = max(0, int(event.get("completed_epochs", 0) or 0))
+            total_epochs = max(1, int(step.arguments.get("epochs", 1)))
+            step.arguments["epochs"] = max(1, total_epochs - completed_epochs)
+            step.arguments["completed_epochs"] = completed_epochs
+            if checkpoint:
+                step.arguments["resume_from"] = checkpoint
+            for pending in remaining_steps:
+                pending.status = StepStatus.PENDING
+            job.plan.steps = remaining_steps
+            job.current_step = -1
+            job.status = JobStatus.QUEUED
+            job.eta_seconds = None
+            job.estimated_completion_at = None
+            self._queue.insert(0, job.id)
+            self._append_log(job, f"Epoch {completed_epochs} checkpoint is complete. Restarting with {updates}.")
+            self.notification.emit("Training settings ready", "Restarting from the completed epoch checkpoint.")
         elif event_type == "failed":
             job.status = JobStatus.FAILED
             job.ended_at = utc_now()
@@ -456,10 +719,35 @@ class JobManager(QObject):
                 event.get("exception"),
                 job.error,
             )
+            self._record_experiment(job)
             self.notification.emit("Job failed", job.error)
         self._save()
         self.job_updated.emit(job)
 
+    def _schedule_job_update(self, job: Job) -> None:
+        self._pending_update_job_ids.add(job.id)
+        if QCoreApplication.instance() is None:
+            self._flush_pending_job_updates()
+            return
+        if self._pending_update_timer_active:
+            return
+        self._pending_update_timer_active = True
+        QTimer.singleShot(300, self._flush_pending_job_updates)
+
+    def _flush_pending_job_updates(self) -> None:
+        if not self._pending_update_job_ids:
+            self._pending_update_timer_active = False
+            return
+        pending_ids = list(self._pending_update_job_ids)
+        self._pending_update_job_ids.clear()
+        self._pending_update_timer_active = False
+        self._save()
+        for job_id in pending_ids:
+            try:
+                self.job_updated.emit(self.get(job_id))
+            except KeyError:
+                continue
+
     def _worker_finished(self) -> None:
         self._worker = None
         self._active_job = None
@@ -468,6 +756,7 @@ class JobManager(QObject):
 
     def supervise(self, snapshot: Any) -> None:
         """Let ATLAS inspect the active training run and apply critical pauses."""
+        self._last_snapshot = snapshot
         job = self._active_job
         if job is None or job.status != JobStatus.RUNNING:
             return
@@ -495,6 +784,14 @@ class JobManager(QObject):
         if decision.action == "pause" and job.status == JobStatus.RUNNING:
             self.pause(job.id)
 
+    def _record_experiment(self, job: Job) -> None:
+        if not self._has_training(job):
+            return
+        try:
+            self.experiments.record_job(job, self._last_snapshot)
+        except Exception as exc:
+            self.logger.warning("Experiment tracking failed for %s: %s", job.id, exc)
+
     def _append_log(self, job: Job, message: str) -> None:
         timestamp = datetime.now().strftime("%H:%M:%S")
         line = f"[{timestamp}] {message}"
@@ -517,13 +814,17 @@ class JobManager(QObject):
             self.jobs = []
             return
         for job in self.jobs:
-            if job.status in {JobStatus.RUNNING, JobStatus.PAUSED, JobStatus.QUEUED}:
+            if job.status in {JobStatus.RUNNING, JobStatus.PAUSED}:
                 job.status = JobStatus.INTERRUPTED
                 job.ended_at = utc_now()
                 job.logs.append(
                     "[startup] Previous session ended before this job. "
                     "Review it before retrying."
                 )
+            elif job.status == JobStatus.QUEUED:
+                self._queue.append(job.id)
+                if not any("Queued job restored" in line for line in job.logs[-5:]):
+                    job.logs.append("[startup] Queued job restored and will run when ADAM is ready.")
         self._save()
 
     def _save(self) -> None:

adam/model_inspector/__init__.py

@@ -0,0 +1,15 @@
+from __future__ import annotations
+
+from .base import ModelComparison, ModelInspection, TensorComparison, TensorStats
+from .comparison import compare_models
+from .detector import inspect_model, inspector_for
+
+__all__ = [
+    "ModelComparison",
+    "ModelInspection",
+    "TensorComparison",
+    "TensorStats",
+    "compare_models",
+    "inspect_model",
+    "inspector_for",
+]

adam/model_inspector/base.py

@@ -0,0 +1,146 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Callable, Iterable
+
+
+MODEL_EXTENSIONS = {".safetensors", ".pt", ".pth", ".bin", ".ckpt"}
+CONFIG_FILENAMES = {
+    "model_index.json",
+    "config.json",
+    "scheduler_config.json",
+    "flow_model_info.json",
+    "adapter_config.json",
+}
+
+
+@dataclass(slots=True)
+class TensorStats:
+    name: str
+    shape: tuple[int, ...]
+    dtype: str
+    parameter_count: int
+    memory_bytes: int
+    minimum: float | None = None
+    maximum: float | None = None
+    mean: float | None = None
+    std: float | None = None
+    abs_mean: float | None = None
+    l2_norm: float | None = None
+    zero_percent: float | None = None
+    component: str = "other"
+    health: list[str] = field(default_factory=list)
+
+
+@dataclass(slots=True)
+class ModelInspection:
+    path: str
+    resolved_path: str
+    architecture: str
+    confidence: float
+    status: str
+    size_bytes: int
+    config_files: list[str]
+    resolution: int | None
+    epoch: int | None
+    step: int | None
+    tensor_count: int
+    total_parameters: int
+    trainable_parameters: int | None
+    parameter_memory_bytes: int
+    dtypes: dict[str, int]
+    components: dict[str, int]
+    largest_tensors: list[TensorStats]
+    tensors: list[TensorStats]
+    health: list[str]
+    messages: list[str]
+    lora: dict[str, Any] = field(default_factory=dict)
+    configs: dict[str, Any] = field(default_factory=dict)
+    histogram: dict[str, list[float]] = field(default_factory=dict)
+    tensor_size_distribution: list[tuple[str, int]] = field(default_factory=list)
+    checkpoints: list[str] = field(default_factory=list)
+    loss_history: list[tuple[int, float]] = field(default_factory=list)
+
+
+@dataclass(slots=True)
+class TensorComparison:
+    name: str
+    shape: tuple[int, ...]
+    component: str
+    mean_abs_difference: float | None
+    relative_difference: float | None
+    cosine_similarity: float | None
+    l2_distance: float | None
+    drift: float | None
+    change_score: float | None
+
+
+@dataclass(slots=True)
+class ModelComparison:
+    path_a: str
+    path_b: str
+    architecture_a: str
+    architecture_b: str
+    architecture_match: bool
+    config_differences: list[str]
+    resolution_difference: tuple[int | None, int | None] | None
+    parameter_count_difference: int
+    only_a: list[str]
+    only_b: list[str]
+    shape_mismatches: list[str]
+    tensor_comparisons: list[TensorComparison]
+    group_comparisons: dict[str, dict[str, float]]
+    messages: list[str]
+
+
+ProgressCallback = Callable[[int, str], None]
+CancelCallback = Callable[[], bool]
+
+
+class InspectorError(RuntimeError):
+    pass
+
+
+class BaseModelInspector:
+    architecture = "Generic / Unknown"
+
+    def inspect(
+        self,
+        path: str | Path,
+        *,
+        recorded_architecture: str = "",
+        run_settings: dict[str, Any] | None = None,
+        progress: ProgressCallback | None = None,
+        cancelled: CancelCallback | None = None,
+    ) -> ModelInspection:
+        raise NotImplementedError
+
+
+def report(progress: ProgressCallback | None, value: int, message: str) -> None:
+    if progress:
+        progress(max(0, min(100, int(value))), message)
+
+
+def is_cancelled(cancelled: CancelCallback | None) -> bool:
+    return bool(cancelled and cancelled())
+
+
+def parameter_count(shape: Iterable[int]) -> int:
+    total = 1
+    for dim in shape:
+        total *= int(dim)
+    return int(total)
+
+
+def dtype_size(dtype: str) -> int:
+    lowered = dtype.casefold()
+    if "float64" in lowered or "int64" in lowered:
+        return 8
+    if "float32" in lowered or "int32" in lowered:
+        return 4
+    if "float16" in lowered or "bfloat16" in lowered or "int16" in lowered:
+        return 2
+    if "int8" in lowered or "uint8" in lowered or "bool" in lowered:
+        return 1
+    return 4

adam/model_inspector/comparison.py

@@ -0,0 +1,160 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+from .base import ModelComparison, TensorComparison, is_cancelled, report
+from .detector import inspect_model
+from .generic import _extract_state_dict, _weight_files
+from .statistics import component_for_name
+
+
+def _iter_named_tensors(path: Path):
+    files = _weight_files(path)
+    for file in files:
+        if file.suffix.casefold() == ".safetensors":
+            from safetensors import safe_open
+
+            with safe_open(str(file), framework="pt", device="cpu") as handle:
+                for key in handle.keys():
+                    yield key, handle.get_tensor(key)
+        else:
+            import torch
+
+            payload = torch.load(str(file), map_location="cpu", weights_only=False)
+            state = _extract_state_dict(payload)
+            for key, value in state.items():
+                if hasattr(value, "shape"):
+                    yield str(key), value
+
+
+def _tensor_map(path: Path) -> dict[str, Any]:
+    return {name: tensor for name, tensor in _iter_named_tensors(path)}
+
+
+def _config_differences(configs_a: dict[str, Any], configs_b: dict[str, Any], *, limit: int = 40) -> list[str]:
+    diffs: list[str] = []
+    keys = sorted(set(configs_a) | set(configs_b))
+    for key in keys:
+        if key not in configs_a:
+            diffs.append(f"Only B has config {key}")
+        elif key not in configs_b:
+            diffs.append(f"Only A has config {key}")
+        elif json.dumps(configs_a[key], sort_keys=True, default=str) != json.dumps(configs_b[key], sort_keys=True, default=str):
+            diffs.append(f"Config differs: {key}")
+        if len(diffs) >= limit:
+            diffs.append("Additional config differences omitted.")
+            break
+    return diffs
+
+
+def compare_models(
+    path_a: str | Path,
+    path_b: str | Path,
+    *,
+    arch_a: str = "",
+    arch_b: str = "",
+    settings_a: dict[str, Any] | None = None,
+    settings_b: dict[str, Any] | None = None,
+    progress=None,
+    cancelled=None,
+) -> ModelComparison:
+    report(progress, 2, "Inspecting first model")
+    summary_a = inspect_model(path_a, recorded_architecture=arch_a, run_settings=settings_a, progress=progress, cancelled=cancelled)
+    report(progress, 30, "Inspecting second model")
+    summary_b = inspect_model(path_b, recorded_architecture=arch_b, run_settings=settings_b, progress=progress, cancelled=cancelled)
+    report(progress, 55, "Loading comparable tensors")
+    tensors_a = _tensor_map(Path(summary_a.resolved_path))
+    if is_cancelled(cancelled):
+        raise RuntimeError("Comparison cancelled.")
+    tensors_b = _tensor_map(Path(summary_b.resolved_path))
+    names_a = set(tensors_a)
+    names_b = set(tensors_b)
+    common = sorted(names_a & names_b)
+    only_a = sorted(names_a - names_b)[:200]
+    only_b = sorted(names_b - names_a)[:200]
+    shape_mismatches = []
+    comparable = []
+    for name in common:
+        if tuple(tensors_a[name].shape) != tuple(tensors_b[name].shape):
+            shape_mismatches.append(name)
+        else:
+            comparable.append(name)
+    comparisons: list[TensorComparison] = []
+    import torch
+
+    for index, name in enumerate(comparable):
+        if is_cancelled(cancelled):
+            raise RuntimeError("Comparison cancelled.")
+        if index % 10 == 0:
+            report(progress, 58 + int(36 * index / max(1, len(comparable))), f"Comparing {index + 1} of {len(comparable)} tensors")
+        with torch.no_grad():
+            a = tensors_a[name].detach().to(device="cpu").float().reshape(-1)
+            b = tensors_b[name].detach().to(device="cpu").float().reshape(-1)
+            if a.numel() == 0:
+                continue
+            limit = 1_000_000
+            if a.numel() > limit:
+                stride = max(1, a.numel() // limit)
+                a = a[::stride][:limit]
+                b = b[::stride][:limit]
+            delta = b - a
+            mean_abs = delta.abs().mean().item()
+            base_abs = a.abs().mean().item()
+            relative = mean_abs / (base_abs + 1e-12)
+            l2 = torch.linalg.vector_norm(delta).item()
+            norm_a = torch.linalg.vector_norm(a).item()
+            norm_b = torch.linalg.vector_norm(b).item()
+            cosine = torch.nn.functional.cosine_similarity(a, b, dim=0).item() if norm_a and norm_b else None
+            drift = (norm_b - norm_a) / (norm_a + 1e-12) if norm_a else None
+            score = relative * 0.7 + (1 - cosine if cosine is not None else 0) * 0.3
+            comparisons.append(
+                TensorComparison(
+                    name=name,
+                    shape=tuple(int(dim) for dim in tensors_a[name].shape),
+                    component=component_for_name(name),
+                    mean_abs_difference=float(mean_abs),
+                    relative_difference=float(relative),
+                    cosine_similarity=float(cosine) if cosine is not None else None,
+                    l2_distance=float(l2),
+                    drift=float(drift) if drift is not None else None,
+                    change_score=float(score),
+                )
+            )
+    comparisons.sort(key=lambda item: item.change_score or 0, reverse=True)
+    groups: dict[str, dict[str, float]] = {}
+    for item in comparisons:
+        group = groups.setdefault(item.component, {"tensors": 0, "mean_change_score": 0.0, "mean_abs_difference": 0.0})
+        group["tensors"] += 1
+        group["mean_change_score"] += item.change_score or 0
+        group["mean_abs_difference"] += item.mean_abs_difference or 0
+    for group in groups.values():
+        count = max(1, int(group["tensors"]))
+        group["mean_change_score"] /= count
+        group["mean_abs_difference"] /= count
+    messages = [
+        "Change Score is a statistical weight-change metric; it does not directly equal behavioral importance."
+    ]
+    if shape_mismatches:
+        messages.append("Some tensor comparisons are unavailable because tensor shapes differ.")
+    report(progress, 100, "Comparison complete")
+    return ModelComparison(
+        path_a=summary_a.resolved_path,
+        path_b=summary_b.resolved_path,
+        architecture_a=summary_a.architecture,
+        architecture_b=summary_b.architecture,
+        architecture_match=summary_a.architecture == summary_b.architecture,
+        config_differences=_config_differences(summary_a.configs, summary_b.configs),
+        resolution_difference=(
+            (summary_a.resolution, summary_b.resolution)
+            if summary_a.resolution != summary_b.resolution else None
+        ),
+        parameter_count_difference=summary_b.total_parameters - summary_a.total_parameters,
+        only_a=only_a,
+        only_b=only_b,
+        shape_mismatches=shape_mismatches[:200],
+        tensor_comparisons=comparisons[:200],
+        group_comparisons=groups,
+        messages=messages,
+    )

adam/model_inspector/ddpm.py

@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from .generic import GenericModelInspector
+
+
+class DDPMInspector(GenericModelInspector):
+    architecture = "DDPM / Diffusers"
+
+    def inspect(self, path: str | Path, *, recorded_architecture: str = "", run_settings: dict[str, Any] | None = None, progress=None, cancelled=None):
+        summary = super().inspect(
+            path,
+            recorded_architecture=recorded_architecture or "ddpm",
+            run_settings=run_settings,
+            progress=progress,
+            cancelled=cancelled,
+        )
+        if summary.architecture == "Generic / Unknown":
+            summary.architecture = self.architecture
+            summary.messages.insert(0, "Model recognized as DDPM from ADAM trainer metadata")
+        expected = ("unet", "scheduler")
+        root = Path(summary.resolved_path)
+        existing = {part.name.casefold() for part in (root.iterdir() if root.is_dir() else [])}
+        for component in expected:
+            if root.is_dir() and component not in existing and not any(component in item.casefold() for item in summary.configs):
+                summary.health.append(f"Unusual: expected DDPM component not found: {component}")
+        return summary

adam/model_inspector/detector.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from .ddpm import DDPMInspector
+from .flow_matching import FlowMatchingInspector
+from .generic import GenericModelInspector
+from .lora import LoRAInspector
+from .maskgit import MaskGITInspector
+
+
+def inspector_for(path: str | Path, recorded_architecture: str = "", settings: dict[str, Any] | None = None):
+    text = " ".join((str(path), recorded_architecture, str(settings or {}))).casefold()
+    target = Path(path).expanduser()
+    if "lora" in text or target.suffix.casefold() == ".safetensors" and "adapter" in target.name.casefold():
+        return LoRAInspector()
+    if "maskgit" in text:
+        return MaskGITInspector()
+    if "flow" in text or (target / "flow_model_info.json").is_file():
+        return FlowMatchingInspector()
+    if "ddpm" in text or (target / "model_index.json").is_file() or (target / "scheduler").is_dir():
+        return DDPMInspector()
+    return GenericModelInspector()
+
+
+def inspect_model(path: str | Path, *, recorded_architecture: str = "", run_settings: dict[str, Any] | None = None, progress=None, cancelled=None):
+    inspector = inspector_for(path, recorded_architecture, run_settings)
+    return inspector.inspect(
+        path,
+        recorded_architecture=recorded_architecture,
+        run_settings=run_settings,
+        progress=progress,
+        cancelled=cancelled,
+    )

adam/model_inspector/flow_matching.py

@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from .generic import GenericModelInspector
+
+
+class FlowMatchingInspector(GenericModelInspector):
+    architecture = "Flow Matching"
+
+    def inspect(self, path: str | Path, *, recorded_architecture: str = "", run_settings: dict[str, Any] | None = None, progress=None, cancelled=None):
+        summary = super().inspect(
+            path,
+            recorded_architecture=recorded_architecture or "flow",
+            run_settings=run_settings,
+            progress=progress,
+            cancelled=cancelled,
+        )
+        summary.architecture = "Flow Matching" if summary.architecture == "Generic / Unknown" else summary.architecture
+        if not any("flow_model_info.json" in item for item in summary.config_files):
+            root = Path(summary.resolved_path)
+            if root.is_dir():
+                summary.health.append("Unusual: Flow Matching metadata file was not found")
+        return summary

adam/model_inspector/generic.py

@@ -0,0 +1,349 @@
+from __future__ import annotations
+
+import json
+from collections import Counter
+from pathlib import Path
+from typing import Any, Iterator
+
+from .base import BaseModelInspector, InspectorError, ModelInspection, TensorStats, is_cancelled, report
+from .statistics import (
+    discover_checkpoint_paths,
+    discover_config_files,
+    step_from_name,
+    tensor_stats_from_torch,
+)
+
+
+def _folder_size(path: Path) -> int:
+    if path.is_file():
+        return path.stat().st_size
+    total = 0
+    try:
+        for item in path.rglob("*"):
+            if item.is_file():
+                total += item.stat().st_size
+    except OSError:
+        return total
+    return total
+
+
+def _read_configs(files: list[Path], root: Path) -> dict[str, Any]:
+    configs: dict[str, Any] = {}
+    for file in files[:40]:
+        try:
+            key = str(file.relative_to(root if root.is_dir() else root.parent))
+        except ValueError:
+            key = file.name
+        try:
+            configs[key] = json.loads(file.read_text(encoding="utf-8"))
+        except (OSError, UnicodeDecodeError, json.JSONDecodeError):
+            configs[key] = "<unreadable>"
+    return configs
+
+
+def _resolution_from_configs(configs: dict[str, Any], settings: dict[str, Any] | None) -> int | None:
+    for source in (settings or {}, *[value for value in configs.values() if isinstance(value, dict)]):
+        for key in ("resolution", "sample_size", "image_size", "size"):
+            value = source.get(key) if isinstance(source, dict) else None
+            if isinstance(value, int):
+                return value
+            if isinstance(value, (list, tuple)) and value and isinstance(value[0], int):
+                return int(value[0])
+            try:
+                if value:
+                    return int(value)
+            except (TypeError, ValueError):
+                pass
+    return None
+
+
+def _iter_safetensors(file: Path) -> Iterator[tuple[str, Any, dict[str, Any]]]:
+    from safetensors import safe_open
+
+    with safe_open(str(file), framework="pt", device="cpu") as handle:
+        metadata = handle.metadata() or {}
+        for key in handle.keys():
+            yield key, handle.get_tensor(key), metadata
+
+
+def _extract_state_dict(payload: Any) -> dict[str, Any]:
+    try:
+        import torch
+    except Exception:
+        torch = None
+    if torch is not None and hasattr(payload, "shape"):
+        return {"tensor": payload}
+    if isinstance(payload, dict):
+        for key in ("state_dict", "model_state_dict", "model", "module", "unet", "network"):
+            value = payload.get(key)
+            if isinstance(value, dict) and any(hasattr(item, "shape") for item in value.values()):
+                return value
+        if any(hasattr(item, "shape") for item in payload.values()):
+            return payload
+    return {}
+
+
+def _iter_torch_checkpoint(file: Path) -> Iterator[tuple[str, Any, dict[str, Any]]]:
+    import torch
+
+    try:
+        payload = torch.load(str(file), map_location="cpu", weights_only=True)
+    except TypeError:
+        payload = torch.load(str(file), map_location="cpu")
+    except Exception:
+        payload = torch.load(str(file), map_location="cpu", weights_only=False)
+    state = _extract_state_dict(payload)
+    metadata = {key: value for key, value in payload.items() if key not in state} if isinstance(payload, dict) else {}
+    for key, tensor in state.items():
+        if hasattr(tensor, "shape"):
+            yield str(key), tensor, metadata
+
+
+def _weight_files(path: Path) -> list[Path]:
+    if path.is_file():
+        return [path]
+    ignored_names = {"optimizer.bin", "scheduler.bin", "scaler.pt"}
+    names = {
+        "diffusion_pytorch_model.safetensors",
+        "model.safetensors",
+        "pytorch_model.bin",
+        "adapter_model.safetensors",
+        "adapter_model.bin",
+        "checkpoint.pt",
+        "best_checkpoint.pt",
+    }
+    files: list[Path] = []
+    try:
+        for item in path.rglob("*"):
+            if item.is_file() and (item.name in names or item.suffix.casefold() in {".safetensors", ".pt", ".pth", ".bin", ".ckpt"}):
+                if item.name.casefold() not in ignored_names:
+                    files.append(item)
+    except OSError:
+        return []
+    if (path / "model_index.json").is_file():
+        final_files = [
+            item for item in files
+            if not any(part.startswith("checkpoint-") for part in item.relative_to(path).parts)
+        ]
+        if final_files:
+            files = final_files
+    return sorted(files, key=lambda item: (0 if item.name in names else 1, str(item)))
+
+
+class GenericModelInspector(BaseModelInspector):
+    architecture = "Generic / Unknown"
+
+    def inspect(
+        self,
+        path: str | Path,
+        *,
+        recorded_architecture: str = "",
+        run_settings: dict[str, Any] | None = None,
+        progress=None,
+        cancelled=None,
+    ) -> ModelInspection:
+        target = Path(path).expanduser()
+        if not target.exists():
+            raise InspectorError(f"Model path does not exist: {target}")
+        target = target.resolve()
+        report(progress, 3, "Finding model files")
+        config_files = discover_config_files(target)
+        configs = _read_configs(config_files, target)
+        files = _weight_files(target)
+        if not files:
+            message = "Model contains no readable tensor checkpoint"
+            return self._empty(target, recorded_architecture, run_settings, config_files, configs, message)
+
+        tensors: list[TensorStats] = []
+        dtypes: Counter[str] = Counter()
+        components: Counter[str] = Counter()
+        health: list[str] = []
+        messages: list[str] = []
+        metadata: dict[str, Any] = {}
+        for file_index, file in enumerate(files):
+            if is_cancelled(cancelled):
+                raise InspectorError("Inspection cancelled.")
+            report(progress, 8 + int(80 * file_index / max(1, len(files))), f"Reading {file.name}")
+            try:
+                if file.suffix.casefold() == ".safetensors":
+                    iterator = _iter_safetensors(file)
+                else:
+                    iterator = _iter_torch_checkpoint(file)
+                for name, tensor, file_metadata in iterator:
+                    if is_cancelled(cancelled):
+                        raise InspectorError("Inspection cancelled.")
+                    prefix = file.parent.name if len(files) > 1 else ""
+                    stat = tensor_stats_from_torch(f"{prefix}.{name}" if prefix and not name.startswith(prefix) else name, tensor)
+                    tensors.append(stat)
+                    dtypes[stat.dtype] += stat.parameter_count
+                    components[stat.component] += stat.parameter_count
+                    health.extend(f"{stat.name}: {item}" for item in stat.health)
+                    if file_metadata:
+                        metadata.update(file_metadata)
+            except Exception as exc:
+                health.append(f"{file.name}: unreadable checkpoint ({exc})")
+
+        if not tensors:
+            message = "Model contains no readable tensor checkpoint"
+            return self._empty(target, recorded_architecture, run_settings, config_files, configs, message, [*(health or []), message])
+
+        report(progress, 92, "Summarizing model")
+        total_parameters = sum(tensor.parameter_count for tensor in tensors)
+        parameter_memory = sum(tensor.memory_bytes for tensor in tensors)
+        largest = sorted(tensors, key=lambda item: item.parameter_count, reverse=True)[:20]
+        architecture, confidence, message = self._architecture_from_signals(
+            target, recorded_architecture, configs, [tensor.name for tensor in tensors]
+        )
+        messages.append(message)
+        duplicate_count = len(tensors) - len({tensor.name for tensor in tensors})
+        if duplicate_count:
+            health.append(f"Unusual: {duplicate_count} duplicate tensor names after folder merging")
+        checkpoints = [str(item) for item in discover_checkpoint_paths(target)]
+        return ModelInspection(
+            path=str(path),
+            resolved_path=str(target),
+            architecture=architecture,
+            confidence=confidence,
+            status="ok",
+            size_bytes=_folder_size(target),
+            config_files=[str(item) for item in config_files],
+            resolution=_resolution_from_configs(configs, run_settings),
+            epoch=self._number_from_metadata(metadata, "epoch"),
+            step=self._number_from_metadata(metadata, "step") or step_from_name(target.name),
+            tensor_count=len(tensors),
+            total_parameters=total_parameters,
+            trainable_parameters=self._trainable_parameters(tensors, architecture),
+            parameter_memory_bytes=parameter_memory,
+            dtypes=dict(dtypes),
+            components=dict(components),
+            largest_tensors=largest,
+            tensors=tensors,
+            health=health or ["No invalid tensor values found in sampled statistics."],
+            messages=messages,
+            lora=self._lora_info(tensors, configs),
+            configs=configs,
+            histogram=self._histogram(tensors),
+            tensor_size_distribution=[(tensor.name, tensor.parameter_count) for tensor in largest],
+            checkpoints=checkpoints,
+            loss_history=[],
+        )
+
+    def _empty(
+        self,
+        target: Path,
+        recorded_architecture: str,
+        run_settings: dict[str, Any] | None,
+        config_files: list[Path],
+        configs: dict[str, Any],
+        message: str,
+        health: list[str] | None = None,
+    ) -> ModelInspection:
+        architecture, confidence, detection_message = self._architecture_from_signals(target, recorded_architecture, configs, [])
+        return ModelInspection(
+            path=str(target),
+            resolved_path=str(target),
+            architecture=architecture,
+            confidence=confidence,
+            status="warning",
+            size_bytes=_folder_size(target),
+            config_files=[str(item) for item in config_files],
+            resolution=_resolution_from_configs(configs, run_settings),
+            epoch=None,
+            step=step_from_name(target.name),
+            tensor_count=0,
+            total_parameters=0,
+            trainable_parameters=None,
+            parameter_memory_bytes=0,
+            dtypes={},
+            components={},
+            largest_tensors=[],
+            tensors=[],
+            health=health or [message],
+            messages=[detection_message, message],
+            configs=configs,
+            checkpoints=[str(item) for item in discover_checkpoint_paths(target)],
+        )
+
+    @staticmethod
+    def _number_from_metadata(metadata: dict[str, Any], key: str) -> int | None:
+        for candidate in (key, f"global_{key}", f"current_{key}"):
+            try:
+                value = metadata.get(candidate)
+                if value is not None:
+                    return int(value)
+            except (TypeError, ValueError):
+                pass
+        return None
+
+    @staticmethod
+    def _trainable_parameters(tensors: list[TensorStats], architecture: str) -> int | None:
+        if architecture == "LoRA":
+            return sum(tensor.parameter_count for tensor in tensors)
+        return None
+
+    @staticmethod
+    def _architecture_from_signals(
+        target: Path,
+        recorded_architecture: str,
+        configs: dict[str, Any],
+        tensor_names: list[str],
+    ) -> tuple[str, float, str]:
+        recorded = recorded_architecture.casefold()
+        joined_names = "\n".join(tensor_names).casefold()
+        config_text = json.dumps(configs, default=str).casefold()
+        folder_text = str(target).casefold()
+        signals = " ".join((joined_names, config_text, folder_text))
+        if "lora" in recorded or "lora" in signals or "adapter_config" in signals:
+            return "LoRA", 0.92, "Model recognized as LoRA"
+        if "maskgit" in recorded or "maskgit" in signals:
+            return "MaskGIT", 0.86, "Model recognized as MaskGIT"
+        if "flow" in recorded or "rectified_flow" in signals or "flow_model_info" in signals:
+            return "Flow Matching", 0.9, "Model recognized as Flow Matching"
+        if "ddpm" in recorded or "diffusers" in config_text or "unet" in signals or "scheduler_config" in signals:
+            return "DDPM / Diffusers", 0.88, "Model recognized as DDPM"
+        return "Generic / Unknown", 0.35, "Model type uncertain - using generic tensor inspection"
+
+    @staticmethod
+    def _lora_info(tensors: list[TensorStats], configs: dict[str, Any]) -> dict[str, Any]:
+        lora_tensors = [tensor for tensor in tensors if "lora" in tensor.name.casefold()]
+        if not lora_tensors:
+            return {}
+        down = [tensor for tensor in lora_tensors if any(token in tensor.name.casefold() for token in ("down", "lora_a"))]
+        up = [tensor for tensor in lora_tensors if any(token in tensor.name.casefold() for token in ("up", "lora_b"))]
+        ranks = sorted({tensor.shape[0] for tensor in down if tensor.shape})
+        alpha = None
+        targets: set[str] = set()
+        for config in configs.values():
+            if isinstance(config, dict):
+                alpha = config.get("lora_alpha", config.get("alpha", alpha))
+                modules = config.get("target_modules")
+                if isinstance(modules, list):
+                    targets.update(str(item) for item in modules)
+        if not targets:
+            for tensor in lora_tensors:
+                parts = tensor.name.split(".")
+                if len(parts) > 2:
+                    targets.add(parts[-3])
+        return {
+            "rank": ", ".join(str(item) for item in ranks[:8]) if ranks else "unknown",
+            "alpha": alpha if alpha is not None else "unknown",
+            "target_modules": sorted(targets)[:20],
+            "down_matrices": len(down),
+            "up_matrices": len(up),
+            "adapter_parameter_count": sum(tensor.parameter_count for tensor in lora_tensors),
+            "average_abs_mean": (
+                sum(tensor.abs_mean or 0 for tensor in lora_tensors) / max(1, len(lora_tensors))
+            ),
+        }
+
+    @staticmethod
+    def _histogram(tensors: list[TensorStats]) -> dict[str, list[float]]:
+        values = [tensor.abs_mean for tensor in tensors if tensor.abs_mean is not None]
+        if not values:
+            return {}
+        buckets = [0.0] * 10
+        high = max(values) or 1.0
+        for value in values:
+            index = min(9, int((value / high) * 10))
+            buckets[index] += 1
+        return {"abs_mean_bins": [round(high * index / 10, 6) for index in range(11)], "counts": buckets}

adam/model_inspector/lora.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from .generic import GenericModelInspector
+
+
+class LoRAInspector(GenericModelInspector):
+    architecture = "LoRA"
+
+    def inspect(self, path: str | Path, *, recorded_architecture: str = "", run_settings: dict[str, Any] | None = None, progress=None, cancelled=None):
+        summary = super().inspect(
+            path,
+            recorded_architecture=recorded_architecture or "lora",
+            run_settings=run_settings,
+            progress=progress,
+            cancelled=cancelled,
+        )
+        summary.architecture = "LoRA" if summary.architecture == "Generic / Unknown" else summary.architecture
+        if summary.tensors and not summary.lora:
+            summary.health.append("Worth inspecting: ADAM marked this as LoRA, but LoRA tensor naming was not obvious")
+        return summary

adam/model_inspector/maskgit.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from .generic import GenericModelInspector
+
+
+class MaskGITInspector(GenericModelInspector):
+    architecture = "MaskGIT"
+
+    def inspect(self, path: str | Path, *, recorded_architecture: str = "", run_settings: dict[str, Any] | None = None, progress=None, cancelled=None):
+        summary = super().inspect(
+            path,
+            recorded_architecture=recorded_architecture or "maskgit",
+            run_settings=run_settings,
+            progress=progress,
+            cancelled=cancelled,
+        )
+        summary.architecture = "MaskGIT" if summary.architecture == "Generic / Unknown" else summary.architecture
+        names = "\n".join(tensor.name for tensor in summary.tensors).casefold()
+        if summary.tensors and not any(token in names for token in ("attention", "attn", "transformer", "embed")):
+            summary.health.append("Worth inspecting: expected MaskGIT transformer or embedding tensors were not obvious")
+        return summary

adam/model_inspector/statistics.py

@@ -0,0 +1,142 @@
+from __future__ import annotations
+
+import math
+from pathlib import Path
+from typing import Any
+
+from .base import CONFIG_FILENAMES, MODEL_EXTENSIONS, TensorStats, dtype_size, parameter_count
+
+
+def bytes_label(size: int | float | None) -> str:
+    if size is None:
+        return "-"
+    value = float(size)
+    for unit in ("B", "KB", "MB", "GB", "TB"):
+        if abs(value) < 1024 or unit == "TB":
+            return f"{value:.1f} {unit}" if unit != "B" else f"{int(value)} B"
+        value /= 1024
+    return f"{value:.1f} TB"
+
+
+def component_for_name(name: str) -> str:
+    lowered = name.casefold()
+    mapping = (
+        ("down_blocks", ("down_blocks", "down.", "downsample")),
+        ("mid_block", ("mid_block", "middle_block", "mid.")),
+        ("up_blocks", ("up_blocks", "up.", "upsample")),
+        ("attention", ("attn", "attention", "to_q", "to_k", "to_v", "query", "key", "value")),
+        ("embeddings", ("embed", "embedding", "position", "token")),
+        ("transformer blocks", ("transformer", "blocks.", "layers.", "encoder", "decoder")),
+        ("output layers", ("out.", "output", "proj_out", "lm_head", "conv_out")),
+        ("LoRA adapters", ("lora", "hada", "lokr", "adapter")),
+        ("normalization", ("norm", "bn", "ln", "group_norm", "layer_norm")),
+    )
+    for component, tokens in mapping:
+        if any(token in lowered for token in tokens):
+            return component
+    return name.split(".", 1)[0] if "." in name else "other"
+
+
+def safe_number(value: Any) -> float | None:
+    try:
+        number = float(value)
+    except (TypeError, ValueError, OverflowError):
+        return None
+    return number if math.isfinite(number) else None
+
+
+def tensor_stats_from_torch(name: str, tensor: Any, *, sample_limit: int = 1_000_000) -> TensorStats:
+    shape = tuple(int(dim) for dim in getattr(tensor, "shape", ()))
+    dtype = str(getattr(tensor, "dtype", "unknown")).replace("torch.", "")
+    count = parameter_count(shape)
+    stat = TensorStats(
+        name=name,
+        shape=shape,
+        dtype=dtype,
+        parameter_count=count,
+        memory_bytes=count * dtype_size(dtype),
+        component=component_for_name(name),
+    )
+    if count == 0:
+        stat.health.append("Empty tensor")
+        return stat
+    try:
+        import torch
+
+        with torch.no_grad():
+            values = tensor.detach().to(device="cpu")
+            if not values.is_floating_point() and not values.is_complex():
+                values = values.float()
+            else:
+                values = values.float()
+            flat = values.reshape(-1)
+            if flat.numel() > sample_limit:
+                stride = max(1, flat.numel() // sample_limit)
+                flat = flat[::stride][:sample_limit]
+            finite = torch.isfinite(flat)
+            if not bool(finite.all()):
+                if bool(torch.isnan(flat).any()):
+                    stat.health.append("Invalid: NaN values found")
+                if bool(torch.isinf(flat).any()):
+                    stat.health.append("Invalid: Inf values found")
+                flat = flat[finite]
+            if flat.numel() == 0:
+                return stat
+            stat.minimum = safe_number(flat.min().item())
+            stat.maximum = safe_number(flat.max().item())
+            stat.mean = safe_number(flat.mean().item())
+            stat.std = safe_number(flat.std(unbiased=False).item()) if flat.numel() > 1 else 0.0
+            stat.abs_mean = safe_number(flat.abs().mean().item())
+            stat.l2_norm = safe_number(torch.linalg.vector_norm(flat).item())
+            stat.zero_percent = safe_number((flat == 0).float().mean().item() * 100)
+    except Exception as exc:
+        stat.health.append(f"Statistics unavailable: {exc}")
+    if stat.abs_mean is not None and stat.abs_mean > 100:
+        stat.health.append("Unusual: very large average weight magnitude")
+    if stat.maximum is not None and stat.minimum is not None and max(abs(stat.maximum), abs(stat.minimum)) > 1_000:
+        stat.health.append("Unusual: very large absolute weight value")
+    return stat
+
+
+def discover_config_files(path: Path) -> list[Path]:
+    root = path if path.is_dir() else path.parent
+    files: list[Path] = []
+    try:
+        for item in root.rglob("*"):
+            if item.is_file() and item.name in CONFIG_FILENAMES:
+                files.append(item)
+    except OSError:
+        return []
+    return sorted(files)
+
+
+def discover_checkpoint_paths(path: Path) -> list[Path]:
+    root = path if path.is_dir() else path.parent
+    candidates: list[Path] = []
+    try:
+        for item in root.rglob("*"):
+            if item.is_file() and item.suffix.casefold() in MODEL_EXTENSIONS:
+                candidates.append(item)
+            elif item.is_dir() and item.name.startswith("checkpoint-"):
+                candidates.append(item)
+    except OSError:
+        return []
+    return sorted(candidates, key=lambda item: (step_from_name(item.name) or -1, str(item)))
+
+
+def step_from_name(name: str) -> int | None:
+    import re
+
+    matches = re.findall(r"(?:step|checkpoint|epoch|e|s)[-_]?(\d+)", name, flags=re.I)
+    if not matches:
+        matches = re.findall(r"(\d+)", name)
+    if not matches:
+        return None
+    try:
+        return int(matches[-1])
+    except ValueError:
+        return None
+
+
+def shape_label(shape: tuple[int, ...]) -> str:
+    return " x ".join(str(dim) for dim in shape) if shape else "scalar"

adam/model_plugin_backend.py

@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+from typing import Any
+
+from adam.executor import ToolContext, ToolExecutionError
+from adam.model_plugins import ModelPluginRegistry, plugin_function, validate_settings
+
+
+def _plugin_for_tool(context: ToolContext, mode: str):
+    registry = ModelPluginRegistry(context.root)
+    for plugin in registry.all():
+        if mode == "training" and plugin.trainer_id == context.tool.id:
+            return plugin
+        if mode == "generation" and plugin.generator_id == context.tool.id:
+            return plugin
+    raise ToolExecutionError(f"No model plugin owns {context.tool.id}.")
+
+
+def train(context: ToolContext, **settings: Any) -> dict[str, Any]:
+    plugin = _plugin_for_tool(context, "training")
+    errors = validate_settings(plugin.training_settings, settings)
+    if errors:
+        raise ToolExecutionError(" ".join(errors))
+    function = plugin_function(plugin, "train")
+    if function is None:
+        raise ToolExecutionError(f"{plugin.name} does not implement train().")
+    return function(settings=settings, callbacks=context) or {}
+
+
+def generate(context: ToolContext, **settings: Any) -> dict[str, Any]:
+    plugin = _plugin_for_tool(context, "generation")
+    errors = validate_settings(plugin.generation_settings, settings)
+    if errors:
+        raise ToolExecutionError(" ".join(errors))
+    function = plugin_function(plugin, "generate")
+    if function is None:
+        raise ToolExecutionError(f"{plugin.name} does not implement generate().")
+    return function(settings=settings, callbacks=context) or {}

adam/model_plugins.py

@@ -0,0 +1,494 @@
+from __future__ import annotations
+
+import importlib
+import importlib.util
+import json
+import logging
+import pkgutil
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Callable
+
+
+REQUIRED_INFO_FIELDS = {"name", "version", "category", "description"}
+SUPPORTED_SETTING_TYPES = {
+    "int",
+    "float",
+    "bool",
+    "choice",
+    "text",
+    "multiline_text",
+    "path",
+    "folder",
+    "slider",
+}
+
+
+class ModelPluginError(RuntimeError):
+    pass
+
+
+@dataclass(frozen=True, slots=True)
+class ModelPlugin:
+    id: str
+    info: dict[str, Any]
+    training_settings: dict[str, dict[str, Any]] = field(default_factory=dict)
+    generation_settings: dict[str, dict[str, Any]] = field(default_factory=dict)
+    training_tool: dict[str, Any] = field(default_factory=dict)
+    generation_tool: dict[str, Any] = field(default_factory=dict)
+    module_name: str = ""
+    plugin_path: Path | None = None
+
+    @property
+    def name(self) -> str:
+        return str(self.info.get("name", self.id))
+
+    @property
+    def trainer_id(self) -> str:
+        return str(self.training_tool.get("id") or f"{self.id}_trainer")
+
+    @property
+    def generator_id(self) -> str:
+        return str(self.generation_tool.get("id") or f"{self.id}_generator")
+
+
+class ModelPluginRegistry:
+    """Discovers model plugins and validates their setting schemas."""
+
+    def __init__(self, root: Path, logger: logging.Logger | None = None) -> None:
+        self.root = root.resolve()
+        self.logger = logger or logging.getLogger(__name__)
+        self.plugins: dict[str, ModelPlugin] = {}
+        self.errors: list[str] = []
+        self.discover()
+
+    def discover(self) -> None:
+        self.plugins = {}
+        self.errors = []
+        for module_name in self._candidate_modules():
+            try:
+                plugin = self._load_module_plugin(module_name)
+            except Exception as exc:
+                message = f"{module_name}: {exc}"
+                self.errors.append(message)
+                self.logger.warning("Model plugin failed to load: %s", message)
+                continue
+            if plugin.id in self.plugins:
+                self.errors.append(f"{module_name}: duplicate model plugin id {plugin.id}")
+                continue
+            self.plugins[plugin.id] = plugin
+
+    def _candidate_modules(self) -> list[str | Path]:
+        modules: list[str | Path] = []
+        try:
+            package = importlib.import_module("adam.model_plugins_builtin")
+            for item in pkgutil.iter_modules(package.__path__, package.__name__ + "."):
+                if not item.ispkg:
+                    continue
+                modules.append(item.name + ".manifest")
+        except Exception as exc:
+            self.errors.append(f"adam.model_plugins_builtin: {exc}")
+
+        models_dir = self.root / "models"
+        if models_dir.is_dir():
+            for folder in sorted(models_dir.iterdir()):
+                manifest = folder / "manifest.py"
+                if not folder.is_dir() or not manifest.is_file():
+                    continue
+                modules.append(manifest)
+        return modules
+
+    def _load_module_plugin(self, module_name: str | Path) -> ModelPlugin:
+        if isinstance(module_name, Path):
+            fallback_id = module_name.parent.name
+            unique_name = f"adam_user_model_{fallback_id}_{abs(hash(str(module_name.resolve())))}"
+            spec = importlib.util.spec_from_file_location(unique_name, module_name)
+            if spec is None or spec.loader is None:
+                raise ModelPluginError(f"Could not load manifest file: {module_name}")
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+            module_label = str(module_name)
+        else:
+            module = importlib.import_module(module_name)
+            fallback_id = module_name.split(".")[-2]
+            module_label = module_name
+        plugin_id = str(getattr(module, "PLUGIN_ID", "") or fallback_id)
+        info = dict(getattr(module, "MODEL_INFO", {}))
+        missing = REQUIRED_INFO_FIELDS - set(info)
+        if missing:
+            raise ModelPluginError(
+                "MODEL_INFO is missing " + ", ".join(sorted(missing))
+            )
+        training_settings = self._validate_schema(
+            dict(getattr(module, "TRAINING_SETTINGS", {})),
+            f"{plugin_id} training",
+        )
+        generation_settings = self._validate_schema(
+            dict(getattr(module, "GENERATION_SETTINGS", {})),
+            f"{plugin_id} generation",
+        )
+        plugin_path = Path(getattr(module, "__file__", "")).resolve().parent
+        return ModelPlugin(
+            id=plugin_id,
+            info=info,
+            training_settings=training_settings,
+            generation_settings=generation_settings,
+            training_tool=dict(getattr(module, "TRAINING_TOOL", {})),
+            generation_tool=dict(getattr(module, "GENERATION_TOOL", {})),
+            module_name=module_label,
+            plugin_path=plugin_path,
+        )
+
+    @staticmethod
+    def _validate_schema(
+        schema: dict[str, Any],
+        label: str,
+    ) -> dict[str, dict[str, Any]]:
+        clean: dict[str, dict[str, Any]] = {}
+        for key, raw in schema.items():
+            if not isinstance(raw, dict):
+                raise ModelPluginError(f"{label} setting {key} must be an object")
+            spec = dict(raw)
+            setting_type = str(spec.get("type", "text"))
+            if setting_type not in SUPPORTED_SETTING_TYPES:
+                raise ModelPluginError(
+                    f"{label} setting {key} has unsupported type {setting_type}"
+                )
+            spec["type"] = setting_type
+            spec.setdefault("label", key.replace("_", " ").title())
+            spec.setdefault("group", "Basic")
+            if setting_type == "choice":
+                options = spec.get("options", [])
+                if not isinstance(options, (list, tuple)) or not options:
+                    raise ModelPluginError(f"{label} setting {key} needs options")
+                spec["options"] = list(options)
+                spec.setdefault("default", spec["options"][0])
+            clean[str(key)] = spec
+        return clean
+
+    def get(self, plugin_id: str) -> ModelPlugin:
+        return self.plugins[plugin_id]
+
+    def all(self) -> list[ModelPlugin]:
+        return list(self.plugins.values())
+
+    def by_trainer(self, trainer: str) -> ModelPlugin | None:
+        return next((plugin for plugin in self.plugins.values() if plugin.id == trainer), None)
+
+    def training_schema(self, trainer: str) -> dict[str, dict[str, Any]]:
+        plugin = self.by_trainer(trainer)
+        return plugin.training_settings if plugin else {}
+
+    def generation_schema_for_tool(self, tool_id: str) -> dict[str, dict[str, Any]]:
+        for plugin in self.plugins.values():
+            if plugin.generator_id == tool_id:
+                return plugin.generation_settings
+        return {}
+
+    def training_tool_specs(self) -> list[dict[str, Any]]:
+        return [
+            self._tool_spec(plugin, mode="training")
+            for plugin in self.plugins.values()
+            if plugin.training_tool
+        ]
+
+    def generation_tool_specs(self) -> list[dict[str, Any]]:
+        return [
+            self._tool_spec(plugin, mode="generation")
+            for plugin in self.plugins.values()
+            if plugin.generation_tool
+        ]
+
+    @staticmethod
+    def _tool_spec(plugin: ModelPlugin, *, mode: str) -> dict[str, Any]:
+        tool = dict(plugin.training_tool if mode == "training" else plugin.generation_tool)
+        schema = plugin.training_settings if mode == "training" else plugin.generation_settings
+        core_arguments = (
+            ["dataset_dir", "model_name", "epochs", "output_dir", "resume_from"]
+            if mode == "training"
+            else [
+                "model_name", "model_path", "prompt", "image_count", "steps",
+                "seed", "sampler", "aspect_ratio",
+            ]
+        )
+        core_required = (
+            ["dataset_dir", "model_name", "epochs", "output_dir"]
+            if mode == "training"
+            else ["model_name", "model_path", "image_count", "steps", "seed"]
+        )
+        defaults = {
+            "id": plugin.trainer_id if mode == "training" else plugin.generator_id,
+            "name": f"{plugin.name} {'Trainer' if mode == 'training' else 'Generator'}",
+            "description": plugin.info.get("description", ""),
+            "category": "Training" if mode == "training" else "Output",
+            "entry_function": "train" if mode == "training" else "generate",
+            "arguments": [*core_arguments, *list(schema)],
+            "required_arguments": [
+                *core_required,
+                *[key for key, spec in schema.items() if bool(spec.get("required"))],
+            ],
+            "capabilities": (
+                ["fresh_training", "progress", "pause", "cancel"]
+                if mode == "training"
+                else ["image_generation", "progress", "cancel"]
+            ),
+            "requires_confirmation": mode == "training",
+            "enabled": True,
+            "demo": False,
+        }
+        defaults.update(tool)
+        defaults["arguments"] = list(defaults.get("arguments") or [*core_arguments, *list(schema)])
+        defaults["required_arguments"] = list(defaults.get("required_arguments") or [])
+        return defaults
+
+    def validate_settings(
+        self,
+        trainer: str,
+        values: dict[str, Any],
+        *,
+        mode: str = "training",
+    ) -> list[str]:
+        plugin = self.by_trainer(trainer)
+        if not plugin:
+            return [f"Unknown model plugin: {trainer}"]
+        schema = plugin.training_settings if mode == "training" else plugin.generation_settings
+        return validate_settings(schema, values)
+
+
+def validate_settings(schema: dict[str, dict[str, Any]], values: dict[str, Any]) -> list[str]:
+    errors: list[str] = []
+    for key, spec in schema.items():
+        value = values.get(key, spec.get("default"))
+        label = str(spec.get("label", key))
+        if spec.get("required") and (value is None or str(value).strip() == ""):
+            errors.append(f"{label} is required.")
+            continue
+        if value in (None, "") and not spec.get("required"):
+            continue
+        setting_type = str(spec.get("type", "text"))
+        try:
+            if setting_type in {"int", "slider"}:
+                if isinstance(value, bool):
+                    raise ValueError
+                numeric = int(value)
+            elif setting_type == "float":
+                if isinstance(value, bool):
+                    raise ValueError
+                numeric = float(value)
+            else:
+                numeric = None
+        except (TypeError, ValueError):
+            errors.append(f"{label} must be a number.")
+            continue
+        if numeric is not None:
+            if "min" in spec and numeric < float(spec["min"]):
+                errors.append(f"{label} must be at least {spec['min']}.")
+            if "max" in spec and numeric > float(spec["max"]):
+                errors.append(f"{label} must be at most {spec['max']}.")
+        if setting_type == "choice" and "options" in spec and value not in spec["options"]:
+            errors.append(f"{label} must be one of: {', '.join(map(str, spec['options']))}.")
+        if setting_type == "path" and spec.get("must_exist") and not Path(str(value)).expanduser().is_file():
+            errors.append(f"{label} must point to an existing file.")
+        if setting_type == "folder" and spec.get("must_exist") and not Path(str(value)).expanduser().is_dir():
+            errors.append(f"{label} must point to an existing folder.")
+    return errors
+
+
+def load_presets(root: Path, plugin_id: str, mode: str) -> dict[str, dict[str, Any]]:
+    path = root.resolve() / "config" / "model_presets.json"
+    try:
+        payload = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return {}
+    presets = payload.get(plugin_id, {}).get(mode, {})
+    return dict(presets) if isinstance(presets, dict) else {}
+
+
+def save_preset(
+    root: Path,
+    plugin_id: str,
+    mode: str,
+    name: str,
+    settings: dict[str, Any],
+) -> None:
+    path = root.resolve() / "config" / "model_presets.json"
+    try:
+        payload = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        payload = {}
+    payload.setdefault(plugin_id, {}).setdefault(mode, {})[name] = settings
+    temporary = path.with_suffix(".tmp")
+    temporary.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")
+    temporary.replace(path)
+
+
+def plugin_function(plugin: ModelPlugin, function_name: str) -> Callable[..., Any] | None:
+    if plugin.module_name.endswith("manifest.py"):
+        spec = importlib.util.spec_from_file_location(
+            f"adam_user_model_{plugin.id}_{abs(hash(plugin.module_name))}",
+            plugin.module_name,
+        )
+        if spec is None or spec.loader is None:
+            return None
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+    else:
+        module = importlib.import_module(plugin.module_name)
+    function = getattr(module, function_name, None)
+    return function if callable(function) else None
+
+
+def safe_plugin_id(name: str) -> str:
+    cleaned = "".join(
+        character.lower() if character.isalnum() else "_"
+        for character in name.strip()
+    )
+    cleaned = "_".join(part for part in cleaned.split("_") if part)
+    return cleaned[:48] or "my_model"
+
+
+def scaffold_model_plugin(
+    root: Path,
+    *,
+    plugin_id: str,
+    name: str,
+    architecture: str = "custom",
+    output_type: str = "image",
+    include_training: bool = True,
+    include_generation: bool = True,
+) -> Path:
+    """Create a simple user-editable model plugin folder."""
+    plugin_id = safe_plugin_id(plugin_id)
+    if plugin_id in {"ddpm", "flow", "lora", "model_template"}:
+        raise ModelPluginError("Choose a plugin id that does not conflict with a built-in model.")
+    folder = root.resolve() / "models" / plugin_id
+    if folder.exists():
+        raise ModelPluginError(f"A model plugin folder already exists: {folder}")
+    folder.mkdir(parents=True)
+    (folder / "__init__.py").write_text(
+        f'"""ADAM model plugin: {name}."""\n',
+        encoding="utf-8",
+    )
+    (folder / "manifest.py").write_text(
+        _manifest_template(
+            plugin_id=plugin_id,
+            name=name,
+            architecture=architecture,
+            output_type=output_type,
+            include_training=include_training,
+            include_generation=include_generation,
+        ),
+        encoding="utf-8",
+    )
+    (folder / "model.py").write_text(_model_template(), encoding="utf-8")
+    if include_training:
+        (folder / "trainer.py").write_text(_trainer_template(), encoding="utf-8")
+    if include_generation:
+        (folder / "generator.py").write_text(_generator_template(), encoding="utf-8")
+    return folder
+
+
+def _manifest_template(
+    *,
+    plugin_id: str,
+    name: str,
+    architecture: str,
+    output_type: str,
+    include_training: bool,
+    include_generation: bool,
+) -> str:
+    plugin_id_json = json.dumps(plugin_id)
+    name_json = json.dumps(name)
+    architecture_json = json.dumps(architecture)
+    output_type_json = json.dumps(output_type)
+    training_tool = (
+        "{\n"
+        f'    "id": "{plugin_id}_trainer",\n'
+        f'    "name": {json.dumps(name + " Trainer")},\n'
+        f'    "backend": {{"type": "python", "module": "models.{plugin_id}.trainer", "function": "train"}},\n'
+        "}"
+        if include_training else "{}"
+    )
+    generation_tool = (
+        "{\n"
+        f'    "id": "{plugin_id}_generator",\n'
+        f'    "name": {json.dumps(name + " Generator")},\n'
+        f'    "model_trainers": ["{plugin_id}"],\n'
+        f'    "backend": {{"type": "python", "module": "models.{plugin_id}.generator", "function": "generate"}},\n'
+        "}"
+        if include_generation else "{}"
+    )
+    return f'''PLUGIN_ID = {plugin_id_json}
+
+MODEL_INFO = {{
+    "name": {name_json},
+    "version": "0.1",
+    "category": "Image Generation",
+    "description": {json.dumps("Describe what " + name + " trains or generates.")},
+    "architecture": {architecture_json},
+    "status": "experimental",
+    "output_type": {output_type_json},
+}}
+
+TRAINING_SETTINGS = {{
+    "resolution": {{"label": "Resolution", "type": "choice", "options": [64, 128, 256, 384, 512], "default": 256, "group": "Basic"}},
+    "batch_size": {{"label": "Batch size", "type": "int", "default": 1, "min": 1, "max": 64, "group": "Basic"}},
+    "learning_rate": {{"label": "Learning rate", "type": "float", "default": 0.0001, "min": 0.0000001, "max": 0.1, "decimals": 7, "group": "Optimization"}},
+    "mixed_precision": {{"label": "Precision", "type": "choice", "options": ["fp16", "no"], "default": "fp16", "group": "Optimization"}},
+    "preview_enabled": {{"label": "Generate previews while training", "type": "bool", "default": True, "group": "Preview"}},
+    "preview_every": {{"label": "Preview interval", "type": "int", "default": 5, "min": 1, "max": 100000, "group": "Preview"}},
+    "preview_prompt": {{"label": "Preview prompt", "type": "text", "default": "", "group": "Preview"}},
+    "preview_seed": {{"label": "Preview seed", "type": "int", "default": 123456789, "min": 0, "max": 2147483647, "group": "Preview"}},
+}}
+
+GENERATION_SETTINGS = {{
+    "prompt": {{"label": "Prompt", "type": "multiline_text", "default": "", "group": "Prompt"}},
+    "image_count": {{"label": "Images", "type": "int", "default": 1, "min": 1, "max": 48, "group": "Generation"}},
+    "steps": {{"label": "Steps", "type": "int", "default": 30, "min": 1, "max": 500, "group": "Generation"}},
+    "seed": {{"label": "Seed", "type": "int", "default": 0, "min": 0, "max": 2147483647, "group": "Generation"}},
+}}
+
+TRAINING_TOOL = {training_tool}
+
+GENERATION_TOOL = {generation_tool}
+'''
+
+
+def _model_template() -> str:
+    return '''from __future__ import annotations
+
+from typing import Any
+
+
+def load_model(model_path: str, settings: dict[str, Any] | None = None) -> Any:
+    """Load your model or inference pipeline here."""
+    raise NotImplementedError("Add your model loading code.")
+'''
+
+
+def _trainer_template() -> str:
+    return '''from __future__ import annotations
+
+from typing import Any
+
+
+def train(context, **settings: Any) -> dict[str, Any]:
+    """Train the model and report progress back to ADAM."""
+    context.log("Replace this with real training code.")
+    context.progress(100, "Training placeholder complete")
+    return {}
+'''
+
+
+def _generator_template() -> str:
+    return '''from __future__ import annotations
+
+from typing import Any
+
+
+def generate(context, **settings: Any) -> dict[str, Any]:
+    """Generate outputs and report progress back to ADAM."""
+    context.log("Replace this with real generation code.")
+    context.progress(100, "Generation placeholder complete")
+    return {}
+'''

adam/model_plugins_builtin/__init__.py

@@ -0,0 +1 @@
+"""Built-in model plugin manifests shipped with ADAM."""

adam/model_plugins_builtin/ddpm/__init__.py

@@ -0,0 +1 @@
+"""DDPM model plugin."""

adam/model_plugins_builtin/ddpm/manifest.py

@@ -0,0 +1,56 @@
+PLUGIN_ID = "ddpm"
+
+MODEL_INFO = {
+    "name": "DDPM",
+    "version": "1.0",
+    "category": "Image Generation",
+    "description": "Denoising Diffusion Probabilistic Model image trainer and generator.",
+    "architecture": "diffusion",
+    "status": "stable",
+    "output_type": "image",
+    "capabilities": ["fresh_training", "resume_training", "image_generation", "smart_generation", "live_preview"],
+    "input_formats": ["image folder"],
+    "output_formats": ["diffusers pipeline", "checkpoint folder", "png preview"],
+    "hardware": {"recommended_vram_gb": 6, "recommended_system_ram_gb": 16},
+    "vram_behavior": {"scales_with": ["resolution", "batch_size"], "estimate": "Moderate; batch size should drop quickly above 256px."},
+}
+
+TRAINING_SETTINGS = {
+    "resolution": {"label": "Resolution", "type": "choice", "options": [64, 128, 256, 384, 512], "default": 128, "group": "Basic"},
+    "batch_size": {"label": "Batch size", "type": "int", "default": 1, "min": 1, "max": 64, "group": "Basic"},
+    "learning_rate": {"label": "Learning rate", "type": "float", "default": 0.0001, "min": 0.0000001, "max": 0.1, "decimals": 7, "step": 0.00005, "group": "Optimization"},
+    "gradient_accumulation_steps": {"label": "Gradient accumulation", "type": "int", "default": 1, "min": 1, "max": 64, "group": "Optimization"},
+    "dataloader_num_workers": {"label": "Loader workers", "type": "int", "default": 4, "min": 0, "max": 16, "group": "Dataset"},
+    "mixed_precision": {"label": "Precision", "type": "choice", "options": ["fp16", "no"], "default": "fp16", "group": "Optimization"},
+    "save_every": {"label": "Save every", "type": "int", "default": 10, "min": 1, "max": 1000, "group": "Checkpoints"},
+    "preview_steps": {"label": "Preview steps", "type": "int", "default": 50, "min": 1, "max": 500, "group": "Preview"},
+    "training_intensity": {"label": "Training intensity", "type": "slider", "default": 100, "min": 10, "max": 100, "group": "Advanced", "advanced": True},
+    "completed_epochs": {"label": "Completed epochs", "type": "int", "default": 0, "min": 0, "max": 100000, "group": "Internal", "advanced": True},
+    "preview_enabled": {"label": "Generate previews while training", "type": "bool", "default": True, "group": "Preview"},
+    "preview_every": {"label": "Preview interval", "type": "int", "default": 5, "min": 1, "max": 100000, "group": "Preview"},
+    "preview_prompt": {"label": "Preview prompt", "type": "text", "default": "", "group": "Preview"},
+    "preview_seed": {"label": "Preview seed", "type": "int", "default": 123456789, "min": 0, "max": 2147483647, "group": "Preview"},
+}
+
+GENERATION_SETTINGS = {
+    "prompt": {"label": "Creative note", "type": "multiline_text", "default": "", "group": "Generation"},
+    "image_count": {"label": "Images", "type": "int", "default": 1, "min": 1, "max": 48, "group": "Generation"},
+    "steps": {"label": "Sampling steps", "type": "int", "default": 50, "min": 5, "max": 500, "group": "Generation"},
+    "sampler": {"label": "Sampler", "type": "choice", "options": ["DDIM", "DDPM"], "default": "DDIM", "group": "Generation"},
+    "aspect_ratio": {"label": "Aspect ratio", "type": "choice", "options": ["1:1 (Square)", "16:9 (Widescreen)", "9:16 (Portrait)", "4:3 (Classic)", "3:4 (Portrait Classic)", "3:2 (Photo)", "2:3 (Portrait Photo)"], "default": "1:1 (Square)", "group": "Generation"},
+    "seed": {"label": "Seed", "type": "int", "default": 0, "min": 0, "max": 2147483647, "group": "Generation"},
+    "reference_image": {"label": "Reference image", "type": "path", "default": "", "group": "Reference"},
+    "reference_strength": {"label": "Reference strength", "type": "slider", "default": 65, "min": 0, "max": 100, "group": "Reference"},
+    "width": {"label": "Custom width", "type": "int", "default": 0, "min": 0, "max": 2048, "group": "Advanced", "advanced": True},
+    "height": {"label": "Custom height", "type": "int", "default": 0, "min": 0, "max": 2048, "group": "Advanced", "advanced": True},
+    "preview_interval": {"label": "Steps per preview", "type": "int", "default": 0, "min": 0, "max": 500, "group": "Preview"},
+    "smart_generation": {"label": "Smart Generation", "type": "bool", "default": False, "group": "Smart Generation"},
+    "smart_wanted_results": {"label": "Wanted results", "type": "int", "default": 8, "min": 1, "max": 48, "group": "Smart Generation"},
+    "smart_max_candidates": {"label": "Maximum candidates", "type": "int", "default": 32, "min": 1, "max": 256, "group": "Smart Generation"},
+    "smart_min_score": {"label": "Minimum score", "type": "float", "default": 0.7, "min": 0, "max": 1, "group": "Smart Generation"},
+    "smart_mode": {"label": "Selection mode", "type": "choice", "options": ["threshold", "top_n"], "default": "threshold", "group": "Smart Generation"},
+    "smart_keep_rejected": {"label": "Keep rejected candidates", "type": "bool", "default": True, "group": "Smart Generation"},
+}
+
+TRAINING_TOOL = {"id": "ddpm_trainer"}
+GENERATION_TOOL = {"id": "ddpm_generator"}

adam/model_plugins_builtin/flow_matching/__init__.py

@@ -0,0 +1 @@
+"""Flow Matching model plugin."""

adam/model_plugins_builtin/flow_matching/manifest.py

@@ -0,0 +1,51 @@
+PLUGIN_ID = "flow"
+
+MODEL_INFO = {
+    "name": "Flow Matching",
+    "version": "1.0",
+    "category": "Image Generation",
+    "description": "Rectified Flow image trainer and generator.",
+    "architecture": "rectified_flow",
+    "status": "stable",
+    "output_type": "image",
+    "capabilities": ["fresh_training", "resume_training", "image_generation", "smart_generation", "live_preview"],
+    "input_formats": ["image folder"],
+    "output_formats": ["diffusers unet folder", "flow metadata", "png preview"],
+    "hardware": {"recommended_vram_gb": 8, "recommended_system_ram_gb": 16},
+    "vram_behavior": {"scales_with": ["resolution", "batch_size"], "estimate": "Moderate to high; flow runs usually want smaller batches at 512px."},
+}
+
+TRAINING_SETTINGS = {
+    "resolution": {"label": "Resolution", "type": "choice", "options": [64, 128, 256, 384, 512], "default": 256, "group": "Basic"},
+    "batch_size": {"label": "Batch size", "type": "int", "default": 8, "min": 1, "max": 64, "group": "Basic"},
+    "learning_rate": {"label": "Learning rate", "type": "float", "default": 0.0002, "min": 0.0000001, "max": 0.1, "decimals": 7, "step": 0.00005, "group": "Optimization"},
+    "gradient_accumulation": {"label": "Gradient accumulation", "type": "int", "default": 1, "min": 1, "max": 64, "group": "Optimization"},
+    "workers": {"label": "Loader workers", "type": "int", "default": 4, "min": 0, "max": 16, "group": "Dataset"},
+    "mixed_precision": {"label": "Precision", "type": "choice", "options": ["fp16", "no"], "default": "fp16", "group": "Optimization"},
+    "save_every": {"label": "Save every", "type": "int", "default": 10, "min": 1, "max": 1000, "group": "Checkpoints"},
+    "preview_steps": {"label": "Preview steps", "type": "int", "default": 10, "min": 1, "max": 500, "group": "Preview"},
+    "gradient_checkpointing": {"label": "Gradient checkpointing", "type": "bool", "default": False, "group": "Advanced", "advanced": True},
+    "preview_enabled": {"label": "Generate previews while training", "type": "bool", "default": True, "group": "Preview"},
+    "preview_every": {"label": "Preview interval", "type": "int", "default": 5, "min": 1, "max": 100000, "group": "Preview"},
+    "preview_prompt": {"label": "Preview prompt", "type": "text", "default": "", "group": "Preview"},
+    "preview_seed": {"label": "Preview seed", "type": "int", "default": 123456789, "min": 0, "max": 2147483647, "group": "Preview"},
+}
+
+GENERATION_SETTINGS = {
+    "prompt": {"label": "Creative note", "type": "multiline_text", "default": "", "group": "Generation"},
+    "image_count": {"label": "Images", "type": "int", "default": 1, "min": 1, "max": 48, "group": "Generation"},
+    "steps": {"label": "ODE steps", "type": "int", "default": 20, "min": 1, "max": 200, "group": "Generation"},
+    "sampler": {"label": "Method", "type": "choice", "options": ["Heun", "Euler"], "default": "Heun", "group": "Generation"},
+    "aspect_ratio": {"label": "Aspect ratio", "type": "choice", "options": ["1:1 (Square)", "4:3 (Landscape)", "3:4 (Portrait)", "3:2 (Landscape)", "2:3 (Portrait)", "16:9 (Widescreen)", "9:16 (Vertical)"], "default": "1:1 (Square)", "group": "Generation"},
+    "seed": {"label": "Seed", "type": "int", "default": 0, "min": 0, "max": 2147483647, "group": "Generation"},
+    "preview_interval": {"label": "Steps per preview", "type": "int", "default": 0, "min": 0, "max": 500, "group": "Preview"},
+    "smart_generation": {"label": "Smart Generation", "type": "bool", "default": False, "group": "Smart Generation"},
+    "smart_wanted_results": {"label": "Wanted results", "type": "int", "default": 8, "min": 1, "max": 48, "group": "Smart Generation"},
+    "smart_max_candidates": {"label": "Maximum candidates", "type": "int", "default": 32, "min": 1, "max": 256, "group": "Smart Generation"},
+    "smart_min_score": {"label": "Minimum score", "type": "float", "default": 0.7, "min": 0, "max": 1, "group": "Smart Generation"},
+    "smart_mode": {"label": "Selection mode", "type": "choice", "options": ["threshold", "top_n"], "default": "threshold", "group": "Smart Generation"},
+    "smart_keep_rejected": {"label": "Keep rejected candidates", "type": "bool", "default": True, "group": "Smart Generation"},
+}
+
+TRAINING_TOOL = {"id": "flow_trainer"}
+GENERATION_TOOL = {"id": "flow_generator"}

adam/model_plugins_builtin/model_template/__init__.py

@@ -0,0 +1 @@
+"""Copyable model plugin template."""

adam/model_plugins_builtin/model_template/manifest.py

@@ -0,0 +1,30 @@
+PLUGIN_ID = "model_template"
+
+MODEL_INFO = {
+    "name": "Model Template",
+    "version": "0.1",
+    "category": "Template",
+    "description": "Example manifest for adding a new ADAM model plugin.",
+    "status": "example",
+}
+
+TRAINING_SETTINGS = {
+    "dataset_dir": {"label": "Dataset folder", "type": "folder", "required": True, "group": "Dataset"},
+    "epochs": {"label": "Epochs", "type": "int", "default": 10, "min": 1, "max": 100000, "group": "Basic"},
+    "learning_rate": {"label": "Learning rate", "type": "float", "default": 0.0001, "min": 0.0000001, "max": 0.1, "group": "Optimization"},
+}
+
+GENERATION_SETTINGS = {
+    "model_path": {"label": "Model file or folder", "type": "path", "required": True, "group": "Model Loading"},
+    "seed": {"label": "Seed", "type": "int", "default": 0, "min": 0, "max": 2147483647, "group": "Generation"},
+}
+
+# A real plugin can point to its own backend:
+# TRAINING_TOOL = {
+#     "backend": {"type": "python", "module": "models.my_model.trainer", "function": "train"},
+# }
+# GENERATION_TOOL = {
+#     "backend": {"type": "python", "module": "models.my_model.generator", "function": "generate"},
+# }
+TRAINING_TOOL = {}
+GENERATION_TOOL = {}

adam/model_plugins_builtin/oasis/__init__.py

@@ -0,0 +1 @@
+"""Oasis action-conditioned world model plugin."""

adam/model_plugins_builtin/oasis/manifest.py

@@ -0,0 +1,70 @@
+PLUGIN_ID = "oasis"
+
+MODEL_INFO = {
+    "name": "Oasis Action World Model",
+    "version": "1.0",
+    "category": "Playable World Models",
+    "description": "Action-conditioned playable world model trainer for gameplay frame sequences.",
+    "architecture": "action_conditioned_rectified_flow_video",
+    "status": "experimental",
+    "output_type": "playable_world",
+    "capabilities": ["fresh_training", "resume_training", "playable_inference", "live_preview"],
+    "input_formats": ["Oasis action dataset folder", "semicolon-separated Oasis dataset folders"],
+    "output_formats": ["action_flow_model_info.json", "diffusers unet folder", "png preview"],
+    "hardware": {"recommended_vram_gb": 12, "recommended_system_ram_gb": 32},
+    "vram_behavior": {
+        "scales_with": ["resolution", "batch_size", "sequence_context"],
+        "estimate": "High; 256x144 with batch 2 is the conservative RTX 3060 starting point.",
+    },
+}
+
+TRAINING_SETTINGS = {
+    "resolution": {"label": "Resolution", "type": "choice", "options": ["128x72", "256x144", "384x216", "512x288"], "default": "256x144", "group": "Basic"},
+    "batch_size": {"label": "Batch size", "type": "int", "default": 2, "min": 1, "max": 16, "group": "Basic"},
+    "learning_rate": {"label": "Learning rate", "type": "float", "default": 0.00002, "min": 0.0000001, "max": 0.01, "decimals": 7, "step": 0.00001, "group": "Optimization"},
+    "workers": {"label": "Loader workers", "type": "int", "default": 2, "min": 0, "max": 8, "group": "Dataset"},
+    "mixed_precision": {"label": "Precision", "type": "choice", "options": ["fp32", "fp16", "no"], "default": "fp32", "group": "Optimization"},
+    "gradient_accumulation": {"label": "Gradient accumulation", "type": "int", "default": 1, "min": 1, "max": 16, "group": "Optimization"},
+    "frame_gap": {"label": "Prediction horizon", "type": "int", "default": 3, "min": 1, "max": 60, "group": "Sequence"},
+    "sequence_context": {"label": "Context length", "type": "int", "default": 1, "min": 1, "max": 32, "group": "Sequence"},
+    "action_aggregation": {"label": "Action aggregation", "type": "choice", "options": ["window", "mean", "last"], "default": "window", "group": "Sequence"},
+    "validation_split": {"label": "Validation split", "type": "float", "default": 0.1, "min": 0.01, "max": 0.5, "decimals": 3, "step": 0.01, "group": "Dataset"},
+    "validation_batches": {"label": "Validation batches", "type": "int", "default": 8, "min": 0, "max": 128, "group": "Dataset"},
+    "save_every": {"label": "Save every", "type": "int", "default": 5, "min": 1, "max": 1000, "group": "Checkpoints"},
+    "preview_enabled": {"label": "Generate previews while training", "type": "bool", "default": True, "group": "Preview"},
+    "preview_every": {"label": "Preview interval", "type": "int", "default": 5, "min": 1, "max": 100000, "group": "Preview"},
+    "preview_steps": {"label": "Preview steps", "type": "int", "default": 1, "min": 1, "max": 50, "group": "Preview"},
+    "seed": {"label": "Random seed", "type": "int", "default": 1234, "min": 0, "max": 2147483647, "group": "Reproducibility"},
+    "base_model": {"label": "Base video model", "type": "folder", "default": "", "group": "Checkpoints", "advanced": True},
+    "condition_noise": {"label": "Condition noise", "type": "float", "default": 0.03, "min": 0.0, "max": 0.5, "decimals": 4, "step": 0.01, "group": "Advanced", "advanced": True},
+    "temporal_loss_weight": {"label": "Temporal loss weight", "type": "float", "default": 0.1, "min": 0.0, "max": 10.0, "decimals": 3, "step": 0.05, "group": "Advanced", "advanced": True},
+    "motion_loss_weight": {"label": "Motion loss weight", "type": "float", "default": 2.0, "min": 0.0, "max": 10.0, "decimals": 3, "step": 0.25, "group": "Advanced", "advanced": True},
+    "action_input_scale": {"label": "Action input scale", "type": "float", "default": 8.0, "min": 0.1, "max": 32.0, "decimals": 3, "step": 0.5, "group": "Advanced", "advanced": True},
+    "neutral_action_dropout": {"label": "Neutral action dropout", "type": "float", "default": 0.15, "min": 0.0, "max": 0.9, "decimals": 3, "step": 0.05, "group": "Advanced", "advanced": True},
+    "action_contrast_weight": {"label": "Action contrast weight", "type": "float", "default": 0.35, "min": 0.0, "max": 5.0, "decimals": 3, "step": 0.05, "group": "Advanced", "advanced": True},
+    "action_contrast_margin": {"label": "Action contrast margin", "type": "float", "default": 0.02, "min": 0.0, "max": 1.0, "decimals": 4, "step": 0.005, "group": "Advanced", "advanced": True},
+    "gradient_checkpointing": {"label": "Gradient checkpointing", "type": "bool", "default": False, "group": "Advanced", "advanced": True},
+    "balance_actions": {"label": "Balance rare actions", "type": "bool", "default": False, "group": "Advanced", "advanced": True},
+}
+
+GENERATION_SETTINGS = {
+    "starting_frame": {"label": "Starting frame", "type": "path", "default": "", "group": "Player"},
+    "seed": {"label": "Seed", "type": "int", "default": 0, "min": 0, "max": 2147483647, "group": "Player"},
+}
+
+TRAINING_TOOL = {
+    "id": "oasis_trainer",
+    "name": "Oasis Action World Model Trainer",
+    "backend": {"type": "python", "module": "adam.tools.oasis_adapter", "function": "train_oasis"},
+    "capabilities": ["fresh_training", "resume_training", "progress", "pause", "cancel", "live_preview"],
+}
+
+GENERATION_TOOL = {
+    "id": "oasis_player",
+    "name": "Oasis Playable Inference",
+    "model_trainers": ["oasis"],
+    "arguments": ["model_name", "model_path", "starting_frame", "seed"],
+    "required_arguments": ["model_path"],
+    "capabilities": ["playable_inference", "keyboard_actions", "progress", "cancel"],
+    "backend": {"type": "python", "module": "adam.tools.oasis_adapter", "function": "launch_oasis_player"},
+}

adam/model_plugins_builtin/sdxl_lora/__init__.py

@@ -0,0 +1 @@
+"""SDXL LoRA model plugin."""

adam/model_plugins_builtin/sdxl_lora/manifest.py

@@ -0,0 +1,60 @@
+PLUGIN_ID = "lora"
+
+MODEL_INFO = {
+    "name": "SDXL LoRA",
+    "version": "1.0",
+    "category": "Image Generation",
+    "description": "Stable Diffusion XL LoRA adapter training and base-model plus adapter generation.",
+    "architecture": "sdxl_lora",
+    "status": "stable",
+    "output_type": "image",
+    "dependencies": ["diffusers", "safetensors"],
+    "capabilities": ["fresh_training", "resume_training", "lora_adapter", "image_generation", "reference_image"],
+    "input_formats": ["captioned image folder", "SDXL checkpoint"],
+    "output_formats": ["safetensors", "png generation"],
+    "hardware": {"recommended_vram_gb": 8, "recommended_system_ram_gb": 16},
+    "vram_behavior": {"scales_with": ["base_model_size", "resolution", "batch_size"], "estimate": "High; SDXL LoRA usually starts safely at batch 1 on 8-12 GB GPUs."},
+}
+
+TRAINING_SETTINGS = {
+    "base_model": {"label": "Base model", "type": "path", "default": "", "required": True, "must_exist": True, "group": "Basic"},
+    "trigger_word": {"label": "Trigger word", "type": "text", "default": "", "group": "LoRA"},
+    "resolution": {"label": "Resolution", "type": "choice", "options": [512, 768, 1024], "default": 1024, "group": "Basic"},
+    "rank": {"label": "Rank", "type": "int", "default": 16, "min": 1, "max": 256, "group": "LoRA"},
+    "alpha": {"label": "Alpha", "type": "int", "default": 16, "min": 1, "max": 256, "group": "LoRA"},
+    "learning_rate": {"label": "Learning rate", "type": "float", "default": 0.0001, "min": 0.0000001, "max": 0.01, "decimals": 7, "step": 0.00005, "group": "Optimization"},
+    "batch_size": {"label": "Batch size", "type": "int", "default": 1, "min": 1, "max": 16, "group": "Basic"},
+    "gradient_accumulation_steps": {"label": "Gradient accumulation", "type": "int", "default": 1, "min": 1, "max": 64, "group": "Optimization"},
+    "mixed_precision": {"label": "Precision", "type": "choice", "options": ["fp16", "bf16", "no"], "default": "fp16", "group": "Optimization"},
+    "caption_extension": {"label": "Caption extension", "type": "choice", "options": [".txt", ".caption"], "default": ".txt", "group": "Dataset"},
+    "save_every": {"label": "Save every", "type": "int", "default": 10, "min": 1, "max": 1000, "group": "Checkpoints"},
+    "optimizer": {"label": "Optimizer", "type": "choice", "options": ["AdamW", "AdamW8bit"], "default": "AdamW", "group": "Optimization", "advanced": True},
+    "gradient_clip_norm": {"label": "Gradient clipping", "type": "float", "default": 1.0, "min": 0.0, "max": 10.0, "decimals": 3, "step": 0.1, "group": "Advanced", "advanced": True},
+    "preview_enabled": {"label": "Generate previews while training", "type": "bool", "default": True, "group": "Preview"},
+    "preview_every": {"label": "Preview interval", "type": "int", "default": 5, "min": 1, "max": 100000, "group": "Preview"},
+    "preview_prompt": {"label": "Preview prompt", "type": "text", "default": "", "group": "Preview"},
+    "preview_seed": {"label": "Preview seed", "type": "int", "default": 123456789, "min": 0, "max": 2147483647, "group": "Preview"},
+}
+
+GENERATION_SETTINGS = {
+    "base_model_path": {"label": "Base model", "type": "path", "default": "", "required": True, "must_exist": True, "group": "Model Loading"},
+    "model_path": {"label": "LoRA adapter", "type": "path", "default": "", "required": True, "must_exist": True, "group": "Model Loading"},
+    "prompt": {"label": "Prompt", "type": "multiline_text", "default": "", "required": True, "group": "Prompt"},
+    "negative_prompt": {"label": "Negative prompt", "type": "multiline_text", "default": "", "group": "Prompt"},
+    "lora_strength": {"label": "LoRA strength", "type": "float", "default": 1.0, "min": 0.0, "max": 2.0, "decimals": 2, "step": 0.05, "group": "Generation"},
+    "image_count": {"label": "Images", "type": "int", "default": 1, "min": 1, "max": 48, "group": "Generation"},
+    "steps": {"label": "Steps", "type": "int", "default": 30, "min": 1, "max": 150, "group": "Generation"},
+    "cfg_scale": {"label": "CFG scale", "type": "float", "default": 7.0, "min": 0.1, "max": 30.0, "decimals": 2, "step": 0.5, "group": "Generation"},
+    "sampler": {"label": "Sampler", "type": "choice", "options": ["DPM++ 2M", "DPM++ SDE", "Euler", "Euler a", "DDIM"], "default": "DPM++ 2M", "group": "Generation"},
+    "aspect_ratio": {"label": "Aspect ratio", "type": "choice", "options": ["1:1 (Square)", "4:3 (Landscape)", "3:4 (Portrait)", "3:2 (Landscape)", "2:3 (Portrait)", "16:9 (Widescreen)", "9:16 (Vertical)"], "default": "1:1 (Square)", "group": "Generation"},
+    "width": {"label": "Width", "type": "int", "default": 1024, "min": 256, "max": 2048, "group": "Generation"},
+    "height": {"label": "Height", "type": "int", "default": 1024, "min": 256, "max": 2048, "group": "Generation"},
+    "seed": {"label": "Seed", "type": "int", "default": 0, "min": 0, "max": 2147483647, "group": "Generation"},
+    "reference_image": {"label": "Reference image", "type": "path", "default": "", "group": "Reference"},
+    "denoise_strength": {"label": "Denoise strength", "type": "float", "default": 0.45, "min": 0.0, "max": 1.0, "decimals": 2, "step": 0.05, "group": "Reference"},
+    "prompt_weighting": {"label": "Use prompt weights", "type": "bool", "default": True, "group": "Advanced", "advanced": True},
+    "preview_interval": {"label": "Steps per preview", "type": "int", "default": 0, "min": 0, "max": 500, "group": "Preview"},
+}
+
+TRAINING_TOOL = {"id": "lora_trainer"}
+GENERATION_TOOL = {"id": "lora_generator"}

adam/model_profiles.py

@@ -0,0 +1,93 @@
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field
+from typing import Any
+
+from adam.model_plugins import ModelPlugin, ModelPluginRegistry
+
+
+@dataclass(frozen=True, slots=True)
+class ModelProfile:
+    """Normalized model profile built from ADAM's plugin manifests."""
+
+    id: str
+    name: str
+    category: str
+    architecture: str
+    version: str
+    description: str
+    status: str = "experimental"
+    output_type: str = "image"
+    training: dict[str, dict[str, Any]] = field(default_factory=dict)
+    generation: dict[str, dict[str, Any]] = field(default_factory=dict)
+    trainer_module: str = ""
+    generator_module: str = ""
+    trainer_tool: str = ""
+    generator_tool: str = ""
+    capabilities: list[str] = field(default_factory=list)
+    hardware: dict[str, Any] = field(default_factory=dict)
+    vram_behavior: dict[str, Any] = field(default_factory=dict)
+    input_formats: list[str] = field(default_factory=list)
+    output_formats: list[str] = field(default_factory=list)
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+def profile_from_plugin(plugin: ModelPlugin) -> ModelProfile:
+    info = dict(plugin.info)
+    training_tool = dict(plugin.training_tool)
+    generation_tool = dict(plugin.generation_tool)
+    capabilities = list(
+        dict.fromkeys(
+            [
+                *info.get("capabilities", []),
+                *training_tool.get("capabilities", []),
+                *generation_tool.get("capabilities", []),
+            ]
+        )
+    )
+    trainer_backend = dict(training_tool.get("backend", {}))
+    generator_backend = dict(generation_tool.get("backend", {}))
+    return ModelProfile(
+        id=plugin.id,
+        name=str(info.get("name", plugin.name)),
+        category=str(info.get("category", "")),
+        architecture=str(info.get("architecture", plugin.id)),
+        version=str(info.get("version", "")),
+        description=str(info.get("description", "")),
+        status=str(info.get("status", "experimental")),
+        output_type=str(info.get("output_type", "image")),
+        training=plugin.training_settings,
+        generation=plugin.generation_settings,
+        trainer_module=str(trainer_backend.get("module", "")),
+        generator_module=str(generator_backend.get("module", "")),
+        trainer_tool=plugin.trainer_id if plugin.training_settings else "",
+        generator_tool=plugin.generator_id if plugin.generation_settings else "",
+        capabilities=capabilities,
+        hardware=dict(info.get("hardware", {})),
+        vram_behavior=dict(info.get("vram_behavior", {})),
+        input_formats=list(info.get("input_formats", [])),
+        output_formats=list(info.get("output_formats", [])),
+    )
+
+
+class ModelProfileRegistry:
+    """Read-only view over plugin manifests for UI and automation features."""
+
+    def __init__(self, plugins: ModelPluginRegistry) -> None:
+        self.plugins = plugins
+
+    def all(self) -> list[ModelProfile]:
+        return [
+            profile_from_plugin(plugin)
+            for plugin in self.plugins.all()
+            if plugin.info.get("category") != "Template"
+        ]
+
+    def get(self, profile_id: str) -> ModelProfile | None:
+        plugin = self.plugins.by_trainer(profile_id)
+        return profile_from_plugin(plugin) if plugin else None
+
+    def as_catalog(self) -> list[dict[str, Any]]:
+        return [profile.to_dict() for profile in self.all()]

adam/models.py

@@ -14,6 +14,7 @@ def utc_now() -> str:
 class JobStatus(str, Enum):
     DRAFT = "Draft"
     AWAITING_CONFIRMATION = "Awaiting confirmation"
+    SCHEDULED = "Scheduled"
     QUEUED = "Queued"
     RUNNING = "Running"
     PAUSED = "Paused"
@@ -73,6 +74,7 @@ class Job:
     progress: int = 0
     current_step: int = -1
     created_at: str = field(default_factory=utc_now)
+    scheduled_for: str | None = None
     started_at: str | None = None
     ended_at: str | None = None
     output_folder: str | None = None
@@ -89,8 +91,15 @@ class Job:
     preview_total: int = 0
     preview_image_index: int = 0
     preview_image_count: int = 0
+    eta_seconds: int | None = None
+    estimated_completion_at: str | None = None
+    progress_current: int = 0
+    progress_total: int = 0
+    progress_rate: float = 0.0
+    progress_unit: str = "step"
     atlas_report: dict[str, Any] = field(default_factory=dict)
     nova_report: dict[str, Any] = field(default_factory=dict)
+    metadata: dict[str, Any] = field(default_factory=dict)
 
     def to_dict(self) -> dict[str, Any]:
         payload = asdict(self)

adam/oasis_dataset.py

@@ -0,0 +1,215 @@
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from PIL import Image
+
+
+BINARY_ACTIONS = {
+    "w", "a", "s", "d", "jump", "arrow_up", "arrow_left", "arrow_down",
+    "arrow_right", "enter", "shift", "ctrl", "alt", "tab", "escape",
+    "q", "e", "r", "f", "z", "x", "c", "v", "key_1", "key_2", "key_3",
+    "key_4", "mouse_left", "mouse_middle", "mouse_right",
+}
+CONTINUOUS_ACTIONS = {"mouse_dx", "mouse_dy", "zoom"}
+DERIVED_ACTIONS = {
+    "move_x", "move_y", "right_mouse", "camera_active",
+    "camera_yaw_delta_degrees", "camera_pitch_delta_degrees",
+    "mouse_raw_dx", "mouse_raw_dy",
+}
+SUPPORTED_ACTIONS = BINARY_ACTIONS | CONTINUOUS_ACTIONS | DERIVED_ACTIONS
+REQUIRED_CANONICAL_ACTIONS = {"w", "a", "s", "d", "jump"}
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
+METADATA_FIELDS = {
+    "session_id", "session_started_at", "frame_index", "filename",
+    "timestamp_seconds", "camera_encoding",
+}
+
+
+@dataclass(slots=True)
+class OasisDatasetReport:
+    dataset_folders: list[str] = field(default_factory=list)
+    frames: int = 0
+    metadata_rows: int = 0
+    valid_rows: int = 0
+    valid_transitions: int = 0
+    sessions: int = 0
+    resolution: str = ""
+    action_counts: dict[str, int] = field(default_factory=dict)
+    errors: list[str] = field(default_factory=list)
+    warnings: list[str] = field(default_factory=list)
+
+    @property
+    def ok(self) -> bool:
+        return not self.errors
+
+
+def dataset_directories(value: str | list[str] | tuple[str, ...]) -> list[Path]:
+    entries = value if isinstance(value, (list, tuple)) else str(value or "").split(";")
+    directories: list[Path] = []
+    for entry in entries:
+        text = str(entry).strip().strip('"')
+        if not text:
+            continue
+        path = Path(text).expanduser()
+        if path not in directories:
+            directories.append(path)
+    return directories
+
+
+def _numeric_frame_index(path: Path) -> int | None:
+    match = re.search(r"frame_(\d+)", path.stem, re.I) or re.search(r"(\d+)", path.stem)
+    return int(match.group(1)) if match else None
+
+
+def validate_oasis_dataset(value: str | list[str] | tuple[str, ...], *, frame_gap: int = 1) -> OasisDatasetReport:
+    report = OasisDatasetReport()
+    frame_gap = max(1, int(frame_gap))
+    directories = dataset_directories(value)
+    if not directories:
+        report.errors.append("Select at least one Oasis action dataset folder.")
+        return report
+    seen_resolution: tuple[int, int] | None = None
+    action_counts = {name: 0 for name in sorted(REQUIRED_CANONICAL_ACTIONS | CONTINUOUS_ACTIONS)}
+    transition_total = 0
+    session_ids: set[str] = set()
+
+    for directory in directories:
+        resolved = directory.resolve()
+        report.dataset_folders.append(str(resolved))
+        if not directory.is_dir():
+            report.errors.append(f"Dataset folder does not exist: {directory}")
+            continue
+        frames_dir = directory / "frames"
+        actions_path = directory / "actions.jsonl"
+        if not frames_dir.is_dir():
+            report.errors.append(f"{directory} is missing a frames folder.")
+            continue
+        if not actions_path.is_file():
+            report.errors.append(f"{directory} is missing actions.jsonl.")
+            continue
+        frame_files = sorted(path for path in frames_dir.iterdir() if path.is_file() and path.suffix.casefold() in IMAGE_EXTENSIONS)
+        report.frames += len(frame_files)
+        if not frame_files:
+            report.errors.append(f"{directory} has no image frames.")
+        indexed_frames = [index for index in (_numeric_frame_index(path) for path in frame_files) if index is not None]
+        frame_paths_by_index: dict[int, list[Path]] = {}
+        for frame_file in frame_files:
+            index = _numeric_frame_index(frame_file)
+            if index is not None:
+                frame_paths_by_index.setdefault(index, []).append(frame_file)
+        if indexed_frames:
+            gaps = [
+                (left, right) for left, right in zip(indexed_frames, indexed_frames[1:])
+                if right != left + 1
+            ]
+            if gaps:
+                report.warnings.append(f"{directory} has frame ordering gaps such as {gaps[0][0]} to {gaps[0][1]}; invalid transitions will be skipped.")
+        rows_by_session: dict[str, list[dict[str, Any]]] = {}
+        seen_keys: set[tuple[str, int]] = set()
+        for line_number, line in enumerate(actions_path.read_text(encoding="utf-8").splitlines(), 1):
+            line = line.strip()
+            if not line:
+                continue
+            report.metadata_rows += 1
+            try:
+                row = json.loads(line)
+            except json.JSONDecodeError:
+                report.errors.append(f"{actions_path.name} line {line_number} is not valid JSON.")
+                continue
+            filename = str(row.get("filename", "")).strip()
+            if not filename:
+                report.errors.append(f"{actions_path.name} line {line_number} has no frame filename.")
+                continue
+            frame_path = frames_dir / filename
+            if not frame_path.is_file():
+                try:
+                    frame_index = int(row.get("frame_index"))
+                except (TypeError, ValueError):
+                    report.warnings.append(
+                        f"{actions_path.name} line {line_number} points to missing frame {filename}; skipping row."
+                    )
+                    continue
+                candidates = frame_paths_by_index.get(frame_index, [])
+                if len(candidates) == 1:
+                    frame_path = candidates[0]
+                    report.warnings.append(
+                        f"{actions_path.name} line {line_number} uses {frame_path.name} for missing legacy filename {filename}."
+                    )
+                else:
+                    report.warnings.append(
+                        f"{actions_path.name} line {line_number} points to missing frame {filename}; skipping row."
+                    )
+                    continue
+            try:
+                with Image.open(frame_path) as image:
+                    image.verify()
+                with Image.open(frame_path) as image:
+                    size = image.size
+            except Exception as exc:
+                report.errors.append(f"Broken image file {frame_path.name}: {exc}")
+                continue
+            if seen_resolution is None:
+                seen_resolution = size
+                report.resolution = f"{size[0]}x{size[1]}"
+            elif size != seen_resolution:
+                report.errors.append(f"Inconsistent frame resolution: {frame_path.name} is {size[0]}x{size[1]}, expected {seen_resolution[0]}x{seen_resolution[1]}.")
+            unexpected = sorted(set(row) - SUPPORTED_ACTIONS - METADATA_FIELDS)
+            if unexpected:
+                report.errors.append(f"{actions_path.name} line {line_number} contains unsupported action field(s): {', '.join(unexpected[:6])}.")
+            missing = sorted(name for name in REQUIRED_CANONICAL_ACTIONS if name not in row)
+            if missing:
+                report.errors.append(f"{actions_path.name} line {line_number} is missing action label(s): {', '.join(missing)}.")
+                continue
+            try:
+                frame_index = int(row.get("frame_index"))
+            except (TypeError, ValueError):
+                report.errors.append(f"{actions_path.name} line {line_number} has an invalid frame_index.")
+                continue
+            session_id = str(row.get("session_id") or f"legacy-{directory.name}").strip()
+            key = (session_id, frame_index)
+            if key in seen_keys:
+                report.errors.append(f"{actions_path.name} repeats frame_index {frame_index} in session {session_id}.")
+                continue
+            seen_keys.add(key)
+            for name in action_counts:
+                try:
+                    value = float(row.get(name, 0))
+                except (TypeError, ValueError):
+                    report.errors.append(f"{actions_path.name} line {line_number} has invalid {name} action value.")
+                    value = 0.0
+                if abs(value) > (0.5 if name in BINARY_ACTIONS else 0.02):
+                    action_counts[name] += 1
+            row["_session_id"] = session_id
+            rows_by_session.setdefault(session_id, []).append(row)
+            session_ids.add(f"{resolved}:{session_id}")
+            report.valid_rows += 1
+        for session_id, rows in rows_by_session.items():
+            if not rows:
+                report.errors.append(f"{directory} has an empty sequence {session_id}.")
+                continue
+            rows.sort(key=lambda item: int(item["frame_index"]))
+            for left, right in zip(rows, rows[1:]):
+                if int(right["frame_index"]) != int(left["frame_index"]) + 1:
+                    report.warnings.append(f"{directory} session {session_id} has an ordering gap at frame {left['frame_index']}; invalid transitions will be skipped.")
+            transition_total += sum(
+                1 for left, right in zip(rows, rows[frame_gap:])
+                if int(right["frame_index"]) == int(left["frame_index"]) + frame_gap
+            )
+
+    report.sessions = len(session_ids)
+    report.action_counts = action_counts
+    report.valid_transitions = transition_total
+    if report.metadata_rows != report.frames:
+        report.warnings.append(f"Frame and label counts differ: {report.frames} frame files, {report.metadata_rows} action rows.")
+    if report.valid_rows < 2:
+        report.errors.append("The dataset needs at least two valid labelled frames.")
+    if report.valid_transitions < 1:
+        report.errors.append(f"No valid frame transitions were found for prediction horizon {frame_gap}.")
+    if report.valid_rows and not any(action_counts.values()):
+        report.errors.append("No non-idle action labels were found. Record idle plus at least one active control.")
+    return report

adam/orion.py

@@ -10,6 +10,8 @@ IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
 
 
 def dataset_image_count(raw_path: object) -> int:
+    if not str(raw_path or "").strip():
+        return 0
     path = Path(str(raw_path or "")).expanduser()
     if not path.is_dir():
         return 0
@@ -36,6 +38,19 @@ def _available_vram_gb() -> float | None:
         return None
 
 
+def _resolution_extent(value: object, fallback: int = 256) -> int:
+    raw = str(value or fallback).strip().lower()
+    if "x" in raw:
+        try:
+            return max(int(part.strip()) for part in raw.split("x", 1))
+        except ValueError:
+            return fallback
+    try:
+        return int(raw)
+    except ValueError:
+        return fallback
+
+
 def recommend_training_settings(
     trainer: str,
     image_count: int,
@@ -46,7 +61,7 @@ def recommend_training_settings(
     """Return an explainable, conservative starting recipe for manual review."""
     trainer = str(trainer).casefold()
     images = max(10, int(image_count))
-    resolution = max(64, min(512, int(resolution)))
+    resolution = max(64, min(512, _resolution_extent(resolution)))
     vram = _available_vram_gb() if vram_gb is None else vram_gb
     cpu_workers = max(2, min(8, (os.cpu_count() or 4) // 2))
 
@@ -131,7 +146,7 @@ def review_training_plan(plan: Any) -> dict[str, Any]:
             1,
             int(args.get("gradient_accumulation_steps", args.get("gradient_accumulation", 1)) or 1),
         )
-        resolution = max(64, int(args.get("resolution", 256) or 256))
+        resolution = max(64, _resolution_extent(args.get("resolution", 256)))
         exposures = images * epochs if images else 0
         optimizer_steps = math.ceil(images / batch / accumulation) * epochs if images else 0
         total_steps += optimizer_steps

adam/planner.py

@@ -43,6 +43,22 @@ def _project_name(subject: str, suffix: str) -> str:
     return f"{safe.title()} {suffix}".strip()[:64]
 
 
+def _trainer_label(trainer: str) -> str:
+    return {
+        "ddpm": "DDPM",
+        "flow": "Flow Matching",
+        "lora": "LoRA",
+        "oasis": "Oasis Action World Model",
+    }.get(trainer, trainer.replace("_", " ").title())
+
+
+def _friendly_model_name(asset: Asset) -> str:
+    name = str(asset.name or "").strip()
+    if re.search(r"^[A-Za-z]:[\\/]", name) or "/" in name or "\\" in name:
+        return Path(asset.path).name
+    return name or Path(asset.path).name
+
+
 def _collection_mode(request: str) -> str:
     """Return the user's requested stopping rule for internet collection."""
     return (
@@ -318,6 +334,10 @@ class Planner:
     def _deterministic_plan(self, request: str) -> ExecutionPlan | None:
         lowered = request.lower()
 
+        oasis_player = self._oasis_player_plan(request)
+        if oasis_player:
+            return oasis_player
+
         youtube_plan = self._youtube_dataset_plan(request)
         if youtube_plan:
             return youtube_plan
@@ -556,10 +576,17 @@ class Planner:
         if not re.search(r"\b(train|fine[- ]?tune|retrain|continue|resume)\b", lowered):
             return None
         fine_tune_payload = self._fine_tune_payload(request)
+        marker = re.search(r"\[ADAM_TRAINER:([A-Za-z0-9_ -]+)\]", request, re.I)
         trainer = str(fine_tune_payload.get("trainer", "")) or (
+            marker.group(1).strip().casefold().replace(" ", "_") if marker else ""
+        ) or (
             "lora" if re.search(r"\blora\b", lowered)
             else "ddpm" if re.search(r"\bddpm\b", lowered)
             else "flow" if re.search(r"\bflow(?:\s+matching)?\b", lowered)
+            else "oasis" if re.search(
+                r"\b(oasis|action[- ]conditioned|playable\s+ai\s+games?|world\s+models?|gameplay[- ]frame|wasd|w/a/s/d)\b",
+                lowered,
+            )
             else ""
         )
         action = (
@@ -574,7 +601,7 @@ class Planner:
         model_query = ""
         resume_match = re.search(
             r"\b(?:fine[- ]?tune|retrain|continue|resume)\s+(?:the\s+)?(.+?)"
-            r"(?:\s+model)?\s+(?:from|on|with)\s+(?:the\s+)?(?:ddpm|lora)\b",
+            r"(?:\s+model)?\s+(?:from|on|with)\s+(?:the\s+)?(?:ddpm|lora|oasis)\b",
             request,
             re.I,
         )
@@ -619,7 +646,7 @@ class Planner:
         if action == "resume_training":
             candidates: list[Asset] = []
             if model_query:
-                candidates = self.assets.find("model", model_query, trainer=trainer)
+                candidates = self._model_candidates(model_query, trainer=trainer)
             if not candidates:
                 return ExecutionPlan(
                     request=request,
@@ -642,7 +669,13 @@ class Planner:
             trainer = trainer or model.trainer
             ddpm_pipeline = trainer == "ddpm" and (Path(model.path) / "model_index.json").is_file()
             flow_model = trainer == "flow" and self._valid_flow_model(Path(model.path))
-            if (not model.checkpoint or not Path(model.checkpoint).exists()) and not ddpm_pipeline and not flow_model:
+            oasis_model = trainer == "oasis" and self._valid_oasis_model(Path(model.path))
+            if (
+                (not model.checkpoint or not Path(model.checkpoint).exists())
+                and not ddpm_pipeline
+                and not flow_model
+                and not oasis_model
+            ):
                 return ExecutionPlan(
                     request=request,
                     summary=(
@@ -653,10 +686,10 @@ class Planner:
                     ),
                     steps=[],
                     project_name="Resume training",
-                )
+            )
             dataset_mode = str(fine_tune_payload.get("dataset_mode", "original"))
             if dataset_mode == "existing":
-                dataset = self._asset_dataset(str(fine_tune_payload.get("dataset_name", "")))
+                dataset = self._resolve_dataset_asset(str(fine_tune_payload.get("dataset_name", "")))
             else:
                 dataset = self._dataset_for_model(model)
             if dataset_mode == "new":
@@ -677,21 +710,25 @@ class Planner:
                     steps=[],
                     project_name="Resume training",
                 )
+            resumed_model_name = _friendly_model_name(model)
             command = TrainingCommand.from_dict(
                 {
                     "action": "resume_training",
                     "trainer": trainer,
                     "dataset": dataset.path,
-                    "model_name": model.name,
+                    "model_name": resumed_model_name,
                     "epochs": epochs,
                     "output": (
-                        str(self._training_output(trainer, f"{model.name} Fine Tune") or model.path)
+                        str(self._training_output(trainer, f"{resumed_model_name} Fine Tune") or model.path)
                         if trainer == "flow" else model.path
                     ),
                     # The DDPM adapter can safely branch from a complete pipeline when
                     # its exact Accelerate checkpoint has been cleaned up.
                     "resume_from": model.checkpoint or model.path,
-                    "base_model": self._lora_base_model() if trainer == "lora" else "",
+                    "base_model": (
+                        str(training_options.get("base_model") or self._lora_base_model())
+                        if trainer == "lora" else ""
+                    ),
                     "training_options": training_options,
                 }
             )
@@ -731,7 +768,10 @@ class Planner:
                     "model_name": model_name,
                     "epochs": epochs,
                     "output": str(output),
-                    "base_model": self._lora_base_model() if trainer == "lora" else "",
+                    "base_model": (
+                        str(training_options.get("base_model") or self._lora_base_model())
+                        if trainer == "lora" else ""
+                    ),
                     "training_options": training_options,
                 }
             )
@@ -787,23 +827,24 @@ class Planner:
         if dataset_dir.exists():
             dataset_dir = dataset_dir.with_name(f"{dataset_dir.name} {datetime.now().strftime('%Y%m%d_%H%M%S')}")
         image_count = max(10, min(int(payload.get("image_count", 60)), 5000))
+        model_name = _friendly_model_name(model)
         arguments: dict[str, Any] = {
-            "dataset_dir": str(dataset_dir), "model_name": model.name,
+            "dataset_dir": str(dataset_dir), "model_name": model_name,
             "epochs": epochs,
             "output_dir": (
-                str(self._training_output(trainer, f"{model.name} Fine Tune") or model.path)
+                str(self._training_output(trainer, f"{model_name} Fine Tune") or model.path)
                 if trainer == "flow" else model.path
             ),
             "resume_from": model.checkpoint or model.path, **training_options,
         }
         if trainer == "lora":
-            base_model = self._lora_base_model()
+            base_model = str(training_options.get("base_model") or self._lora_base_model())
             if not base_model or not Path(base_model).is_file():
                 return ExecutionPlan(request=request, summary="Choose a valid SDXL base model in the LoRA app before fine-tuning.", steps=[], project_name="LoRA training")
             arguments["base_model"] = base_model
         return ExecutionPlan(
             request=request,
-            summary=f"Collect {image_count} new images for {subject}, then continue {model.name} for {epochs} additional epochs.",
+            summary=f"Collect {image_count} new images for {subject}, then continue {model_name} for {epochs} additional epochs.",
             steps=[
                 PlanStep("dataset_collector", "Collect new fine-tune dataset", "Collect and save a reviewable dataset.", {"subject": subject, "image_count": image_count, "collection_mode": "target", "project_name": project, "output_dir": str(dataset_dir)}),
                 PlanStep(f"{trainer}_trainer", f"Fine-tune {trainer.upper()} model", "Continue from the selected saved model using the newly collected dataset.", arguments),
@@ -884,7 +925,7 @@ class Planner:
 
     def _dataset_for_phrase(self, phrase: str) -> Asset | None:
         """Resolve a friendly dataset phrase, preferring the shortest clear folder match."""
-        direct = self._asset_dataset(phrase)
+        direct = self._resolve_dataset_asset(phrase)
         if direct:
             return direct
         wanted = re.sub(r"[^a-z0-9]+", " ", phrase.casefold()).strip()
@@ -900,6 +941,36 @@ class Planner:
         candidates.sort(key=lambda item: (len(item.name), item.name.casefold()))
         return candidates[0]
 
+    def _model_candidates(self, query: str, *, trainer: str = "") -> list[Asset]:
+        raw_query = str(query).strip().strip('"').replace("\\_", "_")
+        path = Path(raw_query).expanduser()
+        if path.exists():
+            resolved = path.resolve()
+            matches = [
+                asset for asset in self.assets.assets
+                if asset.kind == "model"
+                and (not trainer or asset.trainer == trainer)
+                and Path(asset.path).expanduser().resolve() == resolved
+            ]
+            if matches:
+                return matches
+        path_like = re.search(r"^[A-Za-z]:[\\/]", raw_query) or "/" in raw_query or "\\" in raw_query
+        if path_like and path.name:
+            matches = self.assets.find("model", path.name, trainer=trainer)
+            existing = [asset for asset in matches if Path(asset.path).expanduser().exists()]
+            if trainer == "oasis":
+                valid = [
+                    asset for asset in existing
+                    if self._valid_oasis_model(Path(asset.path).expanduser())
+                ]
+                if valid:
+                    return valid
+            if existing:
+                return existing
+            if matches:
+                return matches
+        return self.assets.find("model", raw_query, trainer=trainer)
+
     def _plan_training_command(
         self,
         request: str,
@@ -917,25 +988,48 @@ class Planner:
                 steps=[],
                 project_name="Unsupported training request",
             )
-        dataset_path = Path(command.dataset).expanduser()
-        if not dataset_path.is_dir():
-            raise PlanningError("The validated training dataset does not exist.")
+        if command.trainer == "oasis":
+            dataset_paths = self._oasis_dataset_paths(command.dataset)
+            if not dataset_paths:
+                raise PlanningError(
+                    "The validated Oasis dataset does not exist or contains no action dataset folders."
+                )
+            dataset_path = dataset_paths[0]
+            resolved_dataset_paths = [str(path.resolve()) for path in dataset_paths]
+            dataset_argument = (
+                resolved_dataset_paths[0]
+                if len(resolved_dataset_paths) == 1
+                else resolved_dataset_paths
+            )
+            dataset_label = ";".join(resolved_dataset_paths)
+        else:
+            dataset_path = Path(command.dataset).expanduser()
+            if not dataset_path.is_dir():
+                raise PlanningError("The validated training dataset does not exist.")
+            dataset_argument = str(dataset_path.resolve())
+            dataset_label = dataset_argument
         trainer_folder = self._configured_tool_folder(tool_id)
-        if not trainer_folder:
+        plugin = self.registry.model_plugins.by_trainer(command.trainer)
+        custom_plugin = plugin is not None and command.trainer not in {"ddpm", "flow", "lora", "oasis"}
+        if not trainer_folder and not custom_plugin:
             raise PlanningError(f"The {spec.name} folder is not connected.")
-        output_folder = "output_flow_models" if command.trainer == "flow" else "output"
-        output_root = (Path(trainer_folder) / output_folder).resolve()
         output_path = Path(command.output).expanduser().resolve()
-        try:
-            output_path.relative_to(output_root)
-        except ValueError as exc:
-            raise PlanningError(
-                f"{spec.name} outputs must stay inside {output_root}."
-            ) from exc
+        if custom_plugin:
+            output_root = (self.root / "data" / "model_plugin_outputs" / command.trainer).resolve()
+            output_root.mkdir(parents=True, exist_ok=True)
+        else:
+            output_folder = self._trainer_output_folder(command.trainer)
+            output_root = (Path(trainer_folder) / output_folder).resolve()
+            try:
+                output_path.relative_to(output_root)
+            except ValueError as exc:
+                raise PlanningError(
+                    f"{spec.name} outputs must stay inside {output_root}."
+                ) from exc
         if command.resume_from and not Path(command.resume_from).exists():
             raise PlanningError("The validated resume checkpoint does not exist.")
         arguments: dict[str, Any] = {
-            "dataset_dir": str(dataset_path.resolve()),
+            "dataset_dir": dataset_argument,
             "model_name": command.model_name,
             "epochs": command.epochs,
             "output_dir": str(output_path),
@@ -944,31 +1038,37 @@ class Planner:
         if command.resume_from:
             arguments["resume_from"] = command.resume_from
         if command.trainer == "lora":
-            if not command.base_model or not Path(command.base_model).is_file():
+            base_model = command.base_model or str((command.training_options or {}).get("base_model", ""))
+            if not base_model or not Path(base_model).is_file():
                 return ExecutionPlan(
                     request=request,
                     summary=(
-                        "I found the LoRA dataset, but the connected LoRA trainer has no "
-                        "valid SDXL base model selected. Choose one in the LoRA app first."
+                        "I found the LoRA dataset, but no valid SDXL base model is selected. "
+                        "Choose one in the generated LoRA settings before training."
                     ),
                     steps=[],
                     project_name="LoRA training",
                 )
-            arguments["base_model"] = command.base_model
+            arguments["base_model"] = base_model
+            arguments["trigger_word"] = (
+                command.trigger_word
+                or str((command.training_options or {}).get("trigger_word") or "")
+                or command.model_name
+            )
         verb = "Continue" if command.action == "resume_training" else "Train"
         epoch_kind = "additional epochs" if command.action == "resume_training" else "epochs"
         return ExecutionPlan(
             request=request,
             summary=(
-                f"{verb} {command.model_name} with the registered {command.trainer.upper()} "
-                f"trainer for {command.epochs} {epoch_kind}. Dataset: {command.dataset}. "
+                f"{verb} {command.model_name} with the registered {_trainer_label(command.trainer)} "
+                f"trainer for {command.epochs} {epoch_kind}. Dataset: {dataset_label}. "
                 f"Output: {command.output}."
                 + (f" Training options: {command.training_options}." if command.training_options else "")
             ),
             steps=[
                 PlanStep(
                     tool_id,
-                    f"{verb} {command.trainer.upper()} model",
+                    f"{verb} {_trainer_label(command.trainer)} model",
                     "Launch the connected trainer with validated paths and stream progress.",
                     arguments,
                 )
@@ -987,7 +1087,7 @@ class Planner:
         # causing a valid Flow Matching request to fall back to its old
         # clarification screen instead of creating a training plan.
         explicit_path = re.search(
-            r"\bfrom\s+(?:the\s+)?([A-Za-z]:[\\/].+?)\s+dataset\s*"
+            r"\b(?:from|use)\s+(?:the\s+)?([A-Za-z]:[\\/].+?)\s+dataset\s*"
             r"(?=[,.;]?\s*(?:train|continue|resume|name|call|save|output|put)\b)",
             request,
             re.I,
@@ -996,6 +1096,7 @@ class Planner:
             return explicit_path.group(1).strip()
         patterns = (
             r"\btrain\s+(?:the\s+)?(.+?)\s+dataset\s+(?:on|with|for)\b",
+            r"\buse\s+(?:the\s+)?(.+?)\s+dataset\b",
             r"\bfrom\s+(?:the\s+)?(.+?)\s+dataset\b",
             r"\bwith\s+(?:the\s+)?(.+?)\s+dataset\b",
             r"\b(?:the\s+)?(.+?)\s+dataset\s*,?\s+(?:train|use)\b",
@@ -1015,6 +1116,9 @@ class Planner:
         # metadata before looking for the user-facing name.
         request = re.sub(r"\s*\[ADAM_TRAINING_OPTIONS:\{.*?\}\]", "", request, flags=re.I | re.S)
         match = re.search(r"\b(?:name|call)\s+(?:the\s+)?model\s+(.+?)(?:[,\[\{]|$)", request, re.I)
+        if match:
+            return _clean_subject(match.group(1))
+        match = re.search(r"\b(?:model|checkpoint)\s+called\s+(.+?)(?:[,\[\{]|$)", request, re.I)
         return _clean_subject(match.group(1)) if match else ""
 
     def _asset_dataset(self, name: str) -> Asset | None:
@@ -1047,19 +1151,68 @@ class Planner:
                 return ranked[0]
         return None
 
+    def _resolve_dataset_asset(self, value: str) -> Asset | None:
+        path = self._resolve_dataset(value)
+        if path:
+            return self.assets.register(
+                kind="dataset",
+                name=path.name,
+                path=str(path),
+                persist=False,
+            )
+        return None
+
+    @staticmethod
+    def _is_oasis_dataset_folder(path: Path) -> bool:
+        return path.is_dir() and (path / "frames").is_dir() and (path / "actions.jsonl").is_file()
+
+    def _oasis_dataset_paths(self, value: str) -> list[Path]:
+        paths: list[Path] = []
+        for raw in str(value or "").split(";"):
+            text = raw.strip().strip('"')
+            if not text:
+                continue
+            path = Path(text).expanduser()
+            if self._is_oasis_dataset_folder(path):
+                paths.append(path.resolve())
+                continue
+            if path.is_dir():
+                children = [
+                    child.resolve()
+                    for child in sorted(path.rglob("*"), key=lambda item: str(item).casefold())
+                    if self._is_oasis_dataset_folder(child)
+                ]
+                paths.extend(children)
+        unique: list[Path] = []
+        for path in paths:
+            if path not in unique:
+                unique.append(path)
+        return unique
+
     def _training_output(self, trainer: str, model_name: str) -> Path | None:
         folder = self._configured_tool_folder(f"{trainer}_trainer")
-        if not folder:
+        plugin = self.registry.model_plugins.by_trainer(trainer)
+        if not folder and plugin is None:
             return None
         safe = re.sub(r"[^A-Za-z0-9._-]+", "_", model_name).strip("._") or "model"
-        output_root = "output_flow_models" if trainer == "flow" else "output"
-        candidate = (Path(folder) / output_root / safe).resolve()
+        if folder:
+            output_root = self._trainer_output_folder(trainer)
+            candidate = (Path(folder) / output_root / safe).resolve()
+        else:
+            candidate = (self.root / "data" / "model_plugin_outputs" / trainer / safe).resolve()
         if candidate.exists():
             candidate = candidate.with_name(
                 f"{candidate.name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
             )
         return candidate
 
+    @staticmethod
+    def _trainer_output_folder(trainer: str) -> str:
+        return {
+            "flow": "output_flow_models",
+            "oasis": "output_action_flow_models",
+        }.get(trainer, "output")
+
     @staticmethod
     def _valid_flow_model(folder: Path) -> bool:
         try:
@@ -1145,12 +1298,22 @@ class Planner:
     @staticmethod
     def _training_options_from_request(request: str) -> dict[str, Any]:
         match = re.search(r"\[ADAM_TRAINING_OPTIONS:(\{.*?\})\]", request, re.S)
-        if not match:
-            return {}
-        try:
-            options = json.loads(match.group(1))
-        except json.JSONDecodeError as exc:
-            raise PlanningError("Training options could not be read safely.") from exc
+        options: dict[str, Any] = {}
+        if match:
+            try:
+                parsed = json.loads(match.group(1))
+            except json.JSONDecodeError as exc:
+                raise PlanningError("Training options could not be read safely.") from exc
+            if not isinstance(parsed, dict):
+                raise PlanningError("Training options must be a settings object.")
+            options = parsed
+        trigger_match = re.search(
+            r"\b(?:trigger\s+word|concept\s+token)(?:\s+of|\s*=|\s*:)?\s*['\"\u201c\u201d]?([A-Za-z0-9_.-]{1,128})",
+            request,
+            re.I,
+        )
+        if trigger_match and "trigger_word" not in options:
+            options["trigger_word"] = trigger_match.group(1).strip()
         if not isinstance(options, dict):
             raise PlanningError("Training options must be a settings object.")
         return options
@@ -1475,6 +1638,82 @@ class Planner:
             project_name="DDPM training",
         )
 
+    def _oasis_player_plan(self, request: str) -> ExecutionPlan | None:
+        lowered = request.casefold()
+        if not re.search(r"\b(launch|start|play|open|run)\b", lowered):
+            return None
+        if not re.search(r"\b(oasis|playable\s+ai\s+game|action\s+player|world\s+model)\b", lowered):
+            return None
+        self.assets.discover(self.config)
+        path_match = re.search(r"([A-Za-z]:[\\/][^,\n]+)", request)
+        model_path = Path(path_match.group(1).strip().strip("\"'")) if path_match else None
+        model_name = ""
+        if model_path is not None and not self._valid_oasis_model(model_path):
+            return ExecutionPlan(
+                request=request,
+                summary="I need a valid Oasis action model folder to launch the playable window.",
+                steps=[],
+                project_name="Oasis player",
+            )
+        if model_path is None:
+            query_text = re.sub(r"\b(?:with\s+)?seed\s+\d+\b", " ", request, flags=re.I)
+            query_text = re.sub(r"\b(?:starting|reference)\s+frame\s*(?:is|:|=)?\s*[A-Za-z]:[\\/][^,\n]+", " ", query_text, flags=re.I)
+            query = _clean_subject(
+                re.sub(r"\b(launch|start|play|open|run|oasis|action player|world model)\b", " ", query_text, flags=re.I)
+            )
+            candidates = self.assets.find("model", query, trainer="oasis") if query else [
+                asset for asset in self.assets.assets if asset.kind == "model" and asset.trainer == "oasis"
+            ]
+            candidates = [asset for asset in candidates if Path(asset.path).is_dir()]
+            if len(candidates) != 1:
+                examples = ", ".join(asset.name for asset in candidates[:4])
+                return ExecutionPlan(
+                    request=request,
+                    summary=(
+                        "I need one Oasis checkpoint folder to launch."
+                        + (f" Matching models: {examples}." if examples else "")
+                    ),
+                    steps=[],
+                    project_name="Oasis player",
+                )
+            model_name = candidates[0].name
+            model_path = Path(candidates[0].path)
+        starting_frame = ""
+        start_match = re.search(r"\b(?:starting|reference)\s+frame\s*(?:is|:|=)?\s*([A-Za-z]:[\\/][^,\n]+)", request, re.I)
+        if start_match:
+            starting_frame = start_match.group(1).strip().strip("\"'")
+        seed_match = re.search(r"\bseed\s+(\d+)", request, re.I)
+        return ExecutionPlan(
+            request=request,
+            summary=f"Launch the Oasis playable window for {model_name or model_path.name}.",
+            steps=[
+                PlanStep(
+                    "oasis_player",
+                    "Launch Oasis player",
+                    "Open the existing Oasis playable inference window in its own process.",
+                    {
+                        "model_name": model_name or model_path.name,
+                        "model_path": str(model_path.resolve()),
+                        "starting_frame": starting_frame,
+                        "seed": int(seed_match.group(1)) if seed_match else 0,
+                    },
+                )
+            ],
+            requires_confirmation=False,
+            project_name="Oasis player",
+        )
+
+    @staticmethod
+    def _valid_oasis_model(folder: Path) -> bool:
+        try:
+            metadata = json.loads((folder / "action_flow_model_info.json").read_text(encoding="utf-8"))
+            return (
+                metadata.get("model_type") == "action_conditioned_rectified_flow_video"
+                and (folder / "unet" / "config.json").is_file()
+            )
+        except (OSError, ValueError, TypeError, json.JSONDecodeError):
+            return False
+
     @staticmethod
     def _missing_ddpm_message(fields: dict[str, Any]) -> str:
         missing = [
@@ -1539,17 +1778,31 @@ class Planner:
     def _configured_tool_folder(self, tool_id: str) -> str:
         folders = self.config.get("tool_folders", {})
         if not isinstance(folders, dict):
-            return ""
+            folders = {}
         raw_path = str(folders.get(tool_id, "")).strip()
-        return raw_path if raw_path and Path(raw_path).is_dir() else ""
+        if raw_path and Path(raw_path).is_dir():
+            return raw_path
+        if tool_id == "oasis_trainer":
+            external = self.root / "config" / "external_tools.json"
+            try:
+                payload = json.loads(external.read_text(encoding="utf-8"))
+            except (OSError, ValueError, TypeError, json.JSONDecodeError):
+                return ""
+            for entry in payload.get("tools", []):
+                if isinstance(entry, dict) and entry.get("id") == "external_oasis_game_trainer":
+                    candidate = Path(str(entry.get("backend", {}).get("root", ""))).expanduser()
+                    if candidate.is_dir():
+                        return str(candidate)
+        return ""
 
     def _lora_plan(self, request: str, subject: str) -> ExecutionPlan:
         requested_name = self._model_name_from_request(request)
         model_name = requested_name or subject
+        training_options = self._training_options_from_request(request)
         project = _project_name(model_name, "LoRA")
         collector_root = self._configured_tool_folder("dataset_collector")
         trainer_root = self._configured_tool_folder("lora_trainer")
-        base_model = self._lora_base_model()
+        base_model = str(training_options.get("base_model") or self._lora_base_model())
         if not collector_root or not trainer_root:
             return ExecutionPlan(
                 request=request,
@@ -1564,8 +1817,8 @@ class Planner:
             return ExecutionPlan(
                 request=request,
                 summary=(
-                    "Select a valid SDXL base model in the connected LoRA app first. "
-                    "ADAM will reuse that reviewed setting."
+                    "Choose a valid SDXL base model in the generated LoRA settings "
+                    "before training."
                 ),
                 steps=[],
                 project_name="LoRA training",
@@ -1610,6 +1863,7 @@ class Planner:
                     "epochs": epochs,
                     "output_dir": str(output_dir),
                     "base_model": base_model,
+                    **training_options,
                 },
             ),
         ]

adam/process_control.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import subprocess
+from typing import Any
 
 
 def set_process_tree_paused(process: subprocess.Popen, paused: bool) -> bool:
@@ -19,3 +20,38 @@ def set_process_tree_paused(process: subprocess.Popen, paused: bool) -> bool:
         return True
     except Exception:
         return False
+
+
+def terminate_process_tree(process: Any, *, timeout: float = 3.0) -> None:
+    """Terminate a process and any children it launched."""
+    try:
+        import psutil
+
+        parent = psutil.Process(process.pid)
+        children = parent.children(recursive=True)
+        targets = [*children, parent]
+        for target in targets:
+            try:
+                target.terminate()
+            except (psutil.NoSuchProcess, psutil.AccessDenied):
+                continue
+        _gone, alive = psutil.wait_procs(targets, timeout=timeout)
+        for target in alive:
+            try:
+                target.kill()
+            except (psutil.NoSuchProcess, psutil.AccessDenied):
+                continue
+        return
+    except Exception:
+        pass
+    try:
+        process.terminate()
+    except Exception:
+        return
+    try:
+        process.wait(timeout=timeout)
+    except Exception:
+        try:
+            process.kill()
+        except Exception:
+            pass

adam/recommendations.py

@@ -0,0 +1,195 @@
+from __future__ import annotations
+
+import math
+import os
+from dataclasses import asdict, dataclass, field
+from typing import Any
+
+from adam.model_profiles import ModelProfile
+from adam.models import SystemSnapshot
+
+
+@dataclass(slots=True)
+class SettingsRecommendation:
+    profile_id: str
+    epochs: int
+    settings: dict[str, Any] = field(default_factory=dict)
+    reasons: list[str] = field(default_factory=list)
+    warnings: list[str] = field(default_factory=list)
+    summary: str = ""
+    estimated_vram_gb: float | None = None
+    risk_level: str = "normal"
+    confidence: str = "conservative"
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+def _field_default(profile: ModelProfile, key: str, fallback: Any) -> Any:
+    return profile.training.get(key, {}).get("default", fallback)
+
+
+def _clamp_to_schema(profile: ModelProfile, key: str, value: Any) -> Any:
+    spec = profile.training.get(key, {})
+    kind = str(spec.get("type", "text"))
+    try:
+        if kind in {"int", "slider"}:
+            numeric = int(value)
+            return max(int(spec.get("min", numeric)), min(numeric, int(spec.get("max", numeric))))
+        if kind == "float":
+            numeric = float(value)
+            return max(float(spec.get("min", numeric)), min(numeric, float(spec.get("max", numeric))))
+    except (TypeError, ValueError):
+        return spec.get("default", value)
+    if kind == "choice":
+        options = list(spec.get("options", []))
+        return value if value in options else (options[0] if options else value)
+    return value
+
+
+def estimate_vram_gb(profile: ModelProfile, resolution: int | str, batch_size: int, base_model_gb: float = 0.0) -> float:
+    """Broad VRAM estimate used only for warnings and conservative defaults."""
+    architecture = profile.architecture.casefold()
+    pixels = (max(64, resolution) / 512) ** 2
+    if profile.id == "lora" or "lora" in architecture:
+        base = max(6.0, base_model_gb * 1.8)
+        return base + pixels * max(1, batch_size) * 1.2
+    if profile.id == "oasis" or "action_conditioned" in architecture:
+        width, height = (resolution, resolution)
+        if isinstance(resolution, str) and "x" in resolution:
+            try:
+                width, height = (int(part) for part in resolution.lower().split("x", 1))
+            except ValueError:
+                width, height = (256, 144)
+        pixels = (max(width, height) / 512) ** 2
+        return 4.0 + pixels * max(1, batch_size) * 2.4
+    if "flow" in architecture:
+        return 2.8 + pixels * max(1, batch_size) * 1.0
+    if "diffusion" in architecture:
+        return 2.2 + pixels * max(1, batch_size) * 0.9
+    return 3.0 + pixels * max(1, batch_size) * 0.8
+
+
+def recommend_for_profile(
+    profile: ModelProfile,
+    *,
+    dataset_items: int,
+    resolution: int | str | None = None,
+    snapshot: SystemSnapshot | None = None,
+    base_model_gb: float = 0.0,
+) -> SettingsRecommendation:
+    images = max(10, int(dataset_items or 10))
+    raw_resolution = resolution or _field_default(profile, "resolution", 256) or 256
+    if isinstance(raw_resolution, str) and "x" in raw_resolution:
+        resolution = int(raw_resolution.lower().split("x", 1)[0])
+    else:
+        resolution = int(raw_resolution)
+    reasons: list[str] = []
+    vram_total = snapshot.vram_total_gb if snapshot and snapshot.vram_total_gb else None
+    available_vram = (
+        max(0.0, snapshot.vram_total_gb - snapshot.vram_used_gb)
+        if snapshot and snapshot.vram_total_gb
+        else vram_total
+    )
+    architecture = profile.architecture.casefold()
+    target_exposures = 80_000 if profile.id == "lora" else 180_000 if "diffusion" in architecture else 120_000
+    max_epochs = 220 if profile.id == "lora" else 600 if "diffusion" in architecture else 300
+    epochs = max(10 if profile.id == "lora" else 25, min(max_epochs, round(target_exposures / images)))
+    reasons.append(
+        f"Epochs target roughly {target_exposures:,} image exposures, then clamp to the profile's safe range."
+    )
+
+    batch_defaults = {
+        64: 16,
+        128: 12,
+        256: 4,
+        384: 2,
+        512: 1,
+        768: 1,
+        1024: 1,
+    }
+    if profile.id == "flow":
+        batch_defaults.update({64: 12, 128: 8, 256: 4})
+    if profile.id == "oasis":
+        batch_defaults.update({128: 4, 256: 2, 384: 1, 512: 1})
+    if profile.id == "lora":
+        batch_defaults.update({512: 2, 768: 1, 1024: 1})
+    nearest = min(batch_defaults, key=lambda size: abs(size - resolution))
+    batch_size = batch_defaults[nearest]
+    reasons.append(f"Batch starts from the closest resolution preset ({nearest}px).")
+    if available_vram is not None and available_vram < 8:
+        batch_size = max(1, batch_size // 2)
+        reasons.append("Available VRAM is below 8 GB, so batch size is reduced conservatively.")
+
+    settings: dict[str, Any] = {}
+    for key in ("resolution", "batch_size"):
+        if key in profile.training:
+            value = batch_size
+            if key == "resolution":
+                value = raw_resolution if profile.id == "oasis" else resolution
+            settings[key] = _clamp_to_schema(
+                profile,
+                key,
+                value,
+            )
+    if "learning_rate" in profile.training:
+        settings["learning_rate"] = _clamp_to_schema(
+            profile,
+            "learning_rate",
+            0.00002 if profile.id == "oasis" else 0.0001 if profile.id in {"ddpm", "lora"} else 0.0002,
+        )
+    workers = max(1, min(8, (os.cpu_count() or 4) // 2))
+    for key in ("dataloader_num_workers", "workers"):
+        if key in profile.training:
+            settings[key] = _clamp_to_schema(profile, key, workers)
+    for key, value in {
+        "gradient_accumulation_steps": 1,
+        "gradient_accumulation": 1,
+        "mixed_precision": "fp32" if profile.id == "oasis" else "fp16",
+        "save_every": max(5, min(25, max(1, epochs // 10))),
+        "preview_every": max(5, min(50, max(1, epochs // 10))),
+        "training_intensity": 100,
+        "gradient_checkpointing": resolution >= 384 or (available_vram is not None and available_vram < 8),
+        "rank": 16,
+        "alpha": 16,
+        "frame_gap": 3,
+        "sequence_context": 1,
+        "preview_steps": 1 if profile.id == "oasis" else 50 if profile.id == "ddpm" else 10,
+    }.items():
+        if key in profile.training:
+            settings[key] = _clamp_to_schema(profile, key, value)
+
+    estimated = estimate_vram_gb(profile, resolution, int(settings.get("batch_size", batch_size)), base_model_gb)
+    warnings: list[str] = []
+    if available_vram is not None and estimated > available_vram * 0.9:
+        warnings.append(
+            f"Estimated VRAM need is about {estimated:.1f} GB, above the conservative {available_vram * 0.9:.1f} GB working limit."
+        )
+        if "batch_size" in settings and int(settings["batch_size"]) > 1:
+            settings["batch_size"] = max(1, int(settings["batch_size"]) // 2)
+            estimated = estimate_vram_gb(profile, resolution, int(settings["batch_size"]), base_model_gb)
+            warnings.append(f"Batch size was reduced to {settings['batch_size']} for a safer first run.")
+            reasons.append("The initial VRAM estimate was high, so ADAM reduced the batch before applying the recipe.")
+    if images < 20:
+        warnings.append("Dataset is very small; expect overfitting unless this is just a smoke test.")
+        reasons.append("Very small datasets get a warning because quality usually depends more on data cleanup than long training.")
+    risk_level = "risky" if warnings else "normal"
+
+    memory_note = (
+        f" using about {available_vram:.1f} GB available VRAM" if available_vram is not None else " without detected VRAM"
+    )
+    summary = (
+        f"Recommended {epochs:,} epochs for {images:,} item(s), "
+        f"batch {settings.get('batch_size', batch_size)} at {resolution}px{memory_note}. "
+        "Treat this as a starting recipe, not a guarantee."
+    )
+    return SettingsRecommendation(
+        profile_id=profile.id,
+        epochs=epochs,
+        settings=settings,
+        reasons=reasons,
+        warnings=warnings,
+        summary=summary,
+        estimated_vram_gb=estimated,
+        risk_level=risk_level,
+    )

adam/registry.py

@@ -1,10 +1,13 @@
 from __future__ import annotations
 
 import json
+import logging
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any
 
+from adam.model_plugins import ModelPluginRegistry
+
 
 class RegistryError(RuntimeError):
     pass
@@ -60,6 +63,7 @@ class ToolRegistry:
         self.root = root.resolve()
         self.path = self.root / "config" / "tools.json"
         self._tools: dict[str, ToolSpec] = {}
+        self.model_plugins = ModelPluginRegistry(self.root, logging.getLogger(__name__))
         self.load()
 
     def load(self) -> None:
@@ -112,6 +116,26 @@ class ToolRegistry:
             safe_entry["demo"] = False
             spec = ToolSpec.from_dict(safe_entry)
             loaded[spec.id] = spec
+        for entry in self.model_plugins.training_tool_specs() + self.model_plugins.generation_tool_specs():
+            if not isinstance(entry, dict):
+                continue
+            safe_entry = dict(entry)
+            backend = dict(safe_entry.get("backend", {}))
+            if not backend:
+                safe_entry["backend"] = {
+                    "type": "python",
+                    "module": "adam.model_plugin_backend",
+                    "function": "train" if safe_entry.get("category") == "Training" else "generate",
+                }
+            try:
+                spec = ToolSpec.from_dict(safe_entry)
+            except RegistryError as exc:
+                self.model_plugins.errors.append(f"{safe_entry.get('id', 'unknown')}: {exc}")
+                continue
+            if spec.id in loaded:
+                loaded[spec.id] = _merge_tool_specs(loaded[spec.id], spec)
+            else:
+                loaded[spec.id] = spec
         self._tools = loaded
 
     def get(self, tool_id: str, *, require_enabled: bool = True) -> ToolSpec:
@@ -144,3 +168,31 @@ class ToolRegistry:
             }
             for tool in self.enabled()
         ]
+
+
+def _merge_tool_specs(existing: ToolSpec, plugin: ToolSpec) -> ToolSpec:
+    """Keep the existing backend while accepting plugin-declared schema arguments."""
+    arguments = tuple(dict.fromkeys([*existing.arguments, *plugin.arguments]))
+    required_arguments = existing.required_arguments or plugin.required_arguments
+    capabilities = tuple(dict.fromkeys([*existing.capabilities, *plugin.capabilities]))
+    model_trainers = tuple(
+        dict.fromkeys([*existing.model_trainers, *plugin.model_trainers])
+    )
+    generation_options = dict(existing.generation_options)
+    generation_options.update(plugin.generation_options)
+    return ToolSpec(
+        id=existing.id,
+        name=existing.name,
+        description=existing.description,
+        category=existing.category,
+        entry_function=existing.entry_function,
+        arguments=arguments,
+        required_arguments=required_arguments,
+        capabilities=capabilities,
+        model_trainers=model_trainers,
+        generation_options=generation_options,
+        requires_confirmation=existing.requires_confirmation,
+        enabled=existing.enabled,
+        demo=existing.demo,
+        backend=existing.backend,
+    )

adam/remote_api.py

@@ -0,0 +1,147 @@
+from __future__ import annotations
+
+import json
+import math
+from dataclasses import dataclass
+from typing import Any
+
+
+class RemoteApiError(ValueError):
+    def __init__(self, message: str, *, status: int = 400) -> None:
+        super().__init__(message)
+        self.status = status
+
+
+@dataclass(frozen=True, slots=True)
+class RemoteResponse:
+    status: int
+    body: bytes
+    content_type: str = "application/json"
+    headers: dict[str, str] | None = None
+
+
+def json_response(payload: dict[str, Any], *, status: int = 200) -> RemoteResponse:
+    return RemoteResponse(
+        status=status,
+        body=json.dumps(payload, separators=(",", ":")).encode("utf-8"),
+        content_type="application/json",
+    )
+
+
+def error_response(message: str, *, status: int = 400) -> RemoteResponse:
+    return json_response({"ok": False, "error": message}, status=status)
+
+
+def media_response(body: bytes, content_type: str, *, cache_seconds: int = 86400) -> RemoteResponse:
+    return RemoteResponse(
+        status=200,
+        body=body,
+        content_type=content_type,
+        headers={"Cache-Control": f"private, max-age={max(0, int(cache_seconds))}"},
+    )
+
+
+def bounded_text(value: Any, *, max_length: int, label: str, required: bool = False) -> str:
+    if value is None:
+        value = ""
+    if not isinstance(value, str):
+        value = str(value)
+    text = value.strip()
+    if required and not text:
+        raise RemoteApiError(f"{label} is required.")
+    if len(text) > max_length:
+        raise RemoteApiError(f"{label} must be {max_length} characters or shorter.")
+    return text
+
+
+def bounded_int(
+    value: Any,
+    *,
+    minimum: int,
+    maximum: int,
+    default: int,
+    label: str,
+) -> int:
+    if value in (None, ""):
+        return default
+    try:
+        if isinstance(value, bool):
+            raise ValueError
+        number = int(value)
+    except (TypeError, ValueError) as exc:
+        raise RemoteApiError(f"{label} must be a whole number.") from exc
+    if number < minimum or number > maximum:
+        raise RemoteApiError(f"{label} must be between {minimum} and {maximum}.")
+    return number
+
+
+def bounded_float(
+    value: Any,
+    *,
+    minimum: float,
+    maximum: float,
+    default: float,
+    label: str,
+) -> float:
+    if value in (None, ""):
+        return default
+    try:
+        if isinstance(value, bool):
+            raise ValueError
+        number = float(value)
+    except (TypeError, ValueError) as exc:
+        raise RemoteApiError(f"{label} must be a number.") from exc
+    if not math.isfinite(number) or number < minimum or number > maximum:
+        raise RemoteApiError(f"{label} must be between {minimum:g} and {maximum:g}.")
+    return number
+
+
+def parse_pagination(query: dict[str, list[str]], *, default_size: int = 24, max_size: int = 80) -> dict[str, int]:
+    page = bounded_int(
+        (query.get("page") or ["1"])[0],
+        minimum=1,
+        maximum=1_000_000,
+        default=1,
+        label="Page",
+    )
+    page_size = bounded_int(
+        (query.get("page_size") or [str(default_size)])[0],
+        minimum=1,
+        maximum=max_size,
+        default=default_size,
+        label="Page size",
+    )
+    return {
+        "page": page,
+        "page_size": page_size,
+        "offset": (page - 1) * page_size,
+        "limit": page_size,
+    }
+
+
+def coerce_json_object(payload: Any) -> dict[str, Any]:
+    if not isinstance(payload, dict):
+        raise RemoteApiError("Send a JSON object.")
+    return payload
+
+
+def sanitized_arguments(arguments: dict[str, Any]) -> dict[str, Any]:
+    """Return client-safe arguments without absolute filesystem paths."""
+    hidden = {
+        "dataset_dir",
+        "output_dir",
+        "model_path",
+        "base_model",
+        "base_model_path",
+        "resume_from",
+        "reference_image",
+    }
+    clean: dict[str, Any] = {}
+    for key, value in arguments.items():
+        if key in hidden:
+            text = str(value or "")
+            clean[f"{key}_name"] = text.replace("\\", "/").rstrip("/").rsplit("/", 1)[-1] if text else ""
+            continue
+        if isinstance(value, (str, int, float, bool)) or value is None:
+            clean[key] = value
+    return clean

adam/remote_dashboard.py

@@ -0,0 +1,141 @@
+from __future__ import annotations
+
+
+def remote_dashboard_app_html() -> str:
+    return """<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, viewport-fit=cover">
+<title>ADAM Remote</title>
+<style>
+:root{color-scheme:dark;--bg:#050911;--panel:#111823;--panel2:#171f2b;--line:#26374b;--text:#f4f8ff;--muted:#94a2b1;--blue:#3988ff;--blue2:#74bdff;--gold:#f5c84d;--warn:#f8c35d;--bad:#ff6d86}
+*{box-sizing:border-box}body{margin:0;background:radial-gradient(circle at 50% -20%,#102747 0,#07101c 34%,#02060b 100%);color:var(--text);font:15px/1.38 system-ui,-apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif}button,input,select,textarea{font:inherit}button{border:1px solid #32475d;border-radius:8px;background:#1a2431;color:var(--text);padding:10px 12px;font-weight:750;min-height:42px}button.primary{background:linear-gradient(180deg,#438dff,#2467df);color:#fff;border-color:#4a97ff;box-shadow:0 8px 22px rgba(36,103,223,.28)}button.ghost{background:#121923;color:#c5e4ff;border-color:#2d76c9}button.warn{background:#352816;color:#ffe3a3}button.bad{background:#391d29;color:#ffd2db}button.icon{width:40px;min-width:40px;padding:0;color:var(--gold);font-size:22px;background:transparent;border:0}button:disabled{opacity:.46}input,select,textarea{width:100%;border:1px solid var(--line);border-radius:8px;background:#101722;color:var(--text);min-height:44px;padding:10px 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+@media(min-width:760px){.app{max-width:1020px;margin:0 auto}.wide{grid-template-columns:1.05fr .95fr}.settings-grid{grid-template-columns:1fr 1fr}.tabs{left:50%;transform:translateX(-50%);max-width:1020px;border-left:1px solid var(--line);border-right:1px solid var(--line)}}
+</style>
+</head>
+<body>
+<main class="app">
+  <div class="top"><button id="refresh" class="ghost">Refresh</button><div class="brand"><b>ADAM Remote</b><span id="connection">Connecting</span></div><span></span></div>
+
+  <section id="home" class="view active">
+    <div class="grid wide">
+      <article class="card"><div class="section">Active Job</div><div id="activeJob">Checking ADAM...</div><div class="progress"><div id="activeBar" class="bar"></div></div><div class="row" style="margin-top:10px"><span class="pill">Time Left <b id="timeLeft">-</b></span><span class="pill">Finish <b id="finishTime">-</b></span></div><div class="row" style="margin-top:10px"><button data-action="pause">Pause</button><button data-action="resume">Resume</button><button class="bad" data-action="cancel">Cancel</button></div></article>
+      <article class="card"><div class="section">Live Preview</div><img id="preview" class="preview" alt="Live preview" style="display:none"><div id="previewNote" class="status">Waiting for a preview.</div></article>
+      <article class="card"><div class="section">Prompt ADAM</div><textarea id="prompt" placeholder="Ask ADAM naturally."></textarea><div class="row"><button class="primary" id="sendPrompt">Send</button><button class="ghost" id="askCreate">Create Model</button><button class="ghost" id="askDataset">Collect Dataset</button><button class="ghost" id="askQuick">Quick</button></div><div id="promptStatus" class="status"></div></article>
+      <article class="card"><div class="section">Latest Generation</div><div id="latestGeneration" class="thumbs"></div></article>
+    </div>
+  </section>
+
+  <section id="datasetsView" class="view">
+    <div class="titlebar"><span></span><h1>Datasets</h1><button id="filterDatasets" class="icon" title="Filter">+</button></div>
+    <div class="search-wrap"><span>Q</span><input id="datasetSearch" placeholder="Search datasets..."></div>
+    <div id="locationChips" class="chips" style="margin-top:10px"></div>
+    <div class="section-row"><div class="section">Favorites</div><div class="view-all">View All</div></div><div id="favoriteDatasets" class="dataset-list"></div>
+    <div class="section-row"><div class="section">Recently Used</div><div class="view-all">View All</div></div><div id="recentDatasets" class="dataset-list"></div>
+    <div class="section-row"><div class="section">Locations</div></div><div id="locationsListInline" class="dataset-list"></div>
+    <div class="section-row"><div class="section">All Remembered</div></div><div id="allDatasets" class="dataset-list"></div>
+    <article id="datasetDetail" class="card" style="display:none;margin-top:12px">
+      <div class="section">Preview</div>
+      <div id="datasetTitle"></div><div id="datasetStats" class="meta"></div>
+      <div class="row" style="margin:10px 0"><button id="useDataset" class="primary">Use Dataset</button><button id="selectDatasetForCreate" class="ghost">Select For Create</button></div>
+      <div id="datasetGrid" class="thumbs"></div>
+      <div class="row" style="margin-top:10px"><button id="prevPage">Previous</button><span id="pageLabel" class="pill">Page 1</span><button id="nextPage">Next</button></div>
+    </article>
+    <article id="imageDetail" class="card" style="display:none;margin-top:12px"><img id="detailImage" class="preview" alt="Dataset image"><div id="detailName"></div><div id="detailDims" class="meta"></div><div class="field"><label>Caption</label><textarea id="captionEditor"></textarea></div><div class="row"><button id="saveCaption">Save</button><button id="keepImage">Keep</button><button class="warn" id="rejectImage">Reject</button><button id="unreviewImage">Unreview</button></div><div id="imageStatus" class="status"></div></article>
+  </section>
+
+  <section id="createView" class="view">
+    <div class="titlebar"><button id="createBack" class="icon" title="Back">&lt;</button><h1>Create Model</h1><button id="createHelp" class="icon" title="Help">?</button></div>
+    <article class="panel">
+      <div class="form">
+        <div class="form-row"><label>Preset</label><div><select id="preset"></select><div class="hint">Save time with a preset configuration.</div></div></div>
+        <div class="form-row"><label>Trainer</label><select id="trainer"></select></div>
+        <div class="form-row stack"><label>Model Name</label><input id="modelName" placeholder="Minecraft Oasis V3"></div>
+        <div class="form-row stack"><label>Dataset</label><button id="chooseDataset" class="ghost">Choose Dataset</button><select id="trainDataset" style="display:none"></select><div id="selectedDatasetNote" class="selected-note">No dataset selected</div></div>
+        <div id="baseModelWrap" class="form-row stack"><label>Base model</label><select id="baseModel"></select></div>
+        <div id="basicSettings" class="settings-grid"></div>
+        <details class="details"><summary>Advanced Settings</summary><div id="advancedSettings" class="settings-grid"></div></details>
+        <button id="startTraining" class="primary big-action">Start Training</button>
+        <button id="askInstead" class="ghost big-action">Ask ADAM Instead</button>
+        <button id="reviewPlan" class="ghost">Review Plan</button>
+        <pre id="trainingReview" class="status"></pre>
+      </div>
+    </article>
+  </section>
+
+  <section id="jobsView" class="view"><article class="card"><div class="section">Jobs</div><div id="queues" class="grid"></div></article></section>
+  <section id="settingsView" class="view"><div class="grid"><article class="card"><div class="section">Remote Control</div><label class="toggle"><span>Auto-approve remote training</span><input id="autoApproveTraining" type="checkbox"></label><label class="toggle"><span>Keep screen updated</span><input id="keepAwake" type="checkbox"></label><div id="settingsStatus" class="status"></div></article><article class="card"><div class="section">Remembered Locations</div><div id="locationsList" class="dataset-list"></div></article><article class="card"><div class="section">System</div><div id="system" class="grid two"></div></article></div></section>
+</main>
+<nav class="tabs"><button class="active" data-view="home"><b>^</b>Home</button><button data-view="datasetsView"><b>O</b>Datasets</button><button data-view="createView"><b>+</b>Create</button><button data-view="jobsView"><b>/</b>Jobs</button><button data-view="settingsView"><b>*</b>Settings</button></nav>
+<script>
+(function(){
+var queryString=window.location.search||"";
+var state={datasets:[],locations:[],trainingSchema:{trainers:[],base_models:[],presets:[]},selectedDataset:"",datasetPage:1,selectedItem:null,activeJobId:"",refreshMs:3000,locationFilter:""};
+var timer=null;
+function $(id){return document.getElementById(id)}
+function list(v){return Array.isArray(v)?v:[]}
+function clear(n){if(!n)return;while(n.firstChild)n.removeChild(n.firstChild)}
+function text(id,v){var n=$(id);if(n)n.textContent=v==null?"":String(v)}
+function authUrl(path,extra){var token=queryString.replace(/^\\?/,"");var url=path;if(token)url+=(url.indexOf("?")>=0?"&":"?")+token;if(extra)url+=(url.indexOf("?")>=0?"&":"?")+extra;return url}
+function errorMessage(e){return e&&e.message?e.message:String(e||"Remote request failed.")}
+function requestJson(path,method,payload){return new Promise(function(resolve,reject){var x=new XMLHttpRequest();x.open(method||"GET",authUrl(path),true);x.timeout=25000;x.setRequestHeader("Accept","application/json");if(payload!==undefined)x.setRequestHeader("Content-Type","application/json");x.onreadystatechange=function(){if(x.readyState!==4)return;var p={};try{p=x.responseText?JSON.parse(x.responseText):{}}catch(_e){reject(new Error("ADAM returned an unreadable response."));return}if(x.status<200||x.status>=300){reject(new Error(p.error||"Remote request failed ("+x.status+")."));return}resolve(p)};x.onerror=function(){reject(new Error("Connection failed. Check that ADAM Remote is still running."))};x.ontimeout=function(){reject(new Error("Connection timed out. The PC may be busy."))};x.send(payload===undefined?null:JSON.stringify(payload))})}
+function getJson(path){return requestJson(path,"GET")}function postJson(path,payload){return requestJson(path,"POST",payload||{})}
+function option(select,label,value){var o=document.createElement("option");o.textContent=label||"";o.value=value||"";select.appendChild(o)}
+function appendText(parent,tag,value,cls){var node=document.createElement(tag);node.textContent=value==null?"":String(value);if(cls)node.className=cls;parent.appendChild(node);return node}
+function fieldId(key){return "setting_"+key.replace(/[^A-Za-z0-9_]/g,"_")}
+function activeTrainer(){var id=$("trainer").value;return list(state.trainingSchema.trainers).filter(function(t){return t.id===id})[0]||{settings:{}}}
+function schemaFor(key){return activeTrainer().settings[key]||{}}
+function closestButton(n){while(n&&n!==document.body){if(n.tagName&&n.tagName.toLowerCase()==="button")return n;n=n.parentNode}return null}
+function switchView(id){var nodes=document.querySelectorAll(".view,.tabs button");for(var i=0;i<nodes.length;i++)nodes[i].classList.remove("active");if($(id))$(id).classList.add("active");var tab=document.querySelector('[data-view="'+id+'"]');if(tab)tab.classList.add("active")}
+document.body.addEventListener("click",function(ev){var b=closestButton(ev.target);if(!b)return;var view=b.getAttribute("data-view");if(view)switchView(view);var action=b.getAttribute("data-action");if(action&&state.activeJobId){postJson("/api/job",{job_id:state.activeJobId,action:action}).then(load).catch(function(e){text("promptStatus",errorMessage(e))})}});
+window.addEventListener("error",function(e){text("connection","Phone app error: "+(e.message||"unknown"))});
+window.addEventListener("unhandledrejection",function(e){text("connection","Remote request error: "+errorMessage(e.reason))});
+
+function img(src,alt,note){var im=document.createElement("img");im.alt=alt||"";if(src)im.src=authUrl(src);im.onerror=function(){im.style.display="none";if(note)note.textContent="Preview unavailable"};return im}
+function renderStatus(p){p=p||{};text("connection",(p.app||"ADAM")+" online - "+(p.scope||"remote"));var perms=p.permissions||{};var job=p.active_job||null;state.activeJobId=job&&job.id?job.id:"";if(job){text("activeJob",(job.project||"Active job")+" - "+(job.status||"")+" - "+String(job.progress||0)+"% - Time left "+((job.timing||{}).remaining_label||""));text("timeLeft",(job.timing||{}).remaining_label||"-");text("finishTime",(job.timing||{}).finish_label||"-");$("activeBar").style.width=String(job.progress||0)+"%"}else{text("activeJob","No active job.");text("timeLeft","-");text("finishTime","-");$("activeBar").style.width="0%"}var buttons=document.querySelectorAll("[data-action]");for(var i=0;i<buttons.length;i++)buttons[i].disabled=!job||!perms.job_control;$("autoApproveTraining").checked=!!perms.auto_approve_training;$("autoApproveTraining").disabled=!perms.job_control&&!perms.auto_approve_training;renderPreview(p.preview||{});renderLatest(p.latest_generation||{});renderSystem(p.system||{});renderQueues(p)}
+function renderPreview(info){var p=$("preview");if(info.available){p.style.display="block";p.src=authUrl("/api/preview","t="+Date.now());text("previewNote",(info.kind||"preview")+" "+(info.current||info.epoch||"")+"/"+(info.total||""))}else{p.style.display="none";p.removeAttribute("src");text("previewNote","Waiting for a preview.")}}
+function renderLatest(latest){var root=$("latestGeneration");clear(root);var images=list(latest.images).slice(0,8);if(!latest.available||!images.length){var d=document.createElement("div");d.className="status";d.textContent="Finished generated images will appear here.";root.appendChild(d);return}images.forEach(function(item){var b=document.createElement("button");b.className="thumb";b.type="button";var n=document.createElement("div");n.textContent=latest.model_name||"Generated image";b.appendChild(img(item.url,"Generated image",n));b.appendChild(n);b.onclick=function(){window.open(authUrl(item.url,"t="+Date.now()),"_blank","noopener,noreferrer")};root.appendChild(b)})}
+function renderSystem(sys){var root=$("system");clear(root);[["CPU",sys.cpu_percent],["RAM",sys.memory_percent],["GPU",sys.gpu_percent],["VRAM",sys.vram_percent]].forEach(function(m){var d=document.createElement("div");d.className="card";appendText(d,"div",m[0],"meta");appendText(d,"b",m[1]==null?"-":m[1]+"%");root.appendChild(d)})}
+function renderQueues(p){var root=$("queues");clear(root);var jobs=list(p.queue).concat(list(p.completed_jobs),list(p.failed_jobs)).slice(0,40);if(!jobs.length){text("queues","No jobs yet.");return}jobs.forEach(function(job){var d=document.createElement("div");d.className="queue-item";appendText(d,"b",job.project||"ADAM Job");appendText(d,"div",(job.status||"")+" - "+String(job.progress||0)+"%","meta");if(job.current_step_title){var s=document.createElement("div");s.className="meta";s.textContent=job.current_step_title;d.appendChild(s)}if(job.error){var e=document.createElement("div");e.className="bad-text";e.textContent=job.error;d.appendChild(e)}root.appendChild(d)})}
+
+function load(){return getJson("/api/status").then(renderStatus).catch(function(e){text("connection","Offline: "+errorMessage(e))})}
+function loadData(){return Promise.all([
+  getJson("/api/v1/datasets").then(function(p){state.datasets=list(p.datasets);renderDatasets();fillDatasets()}),
+  getJson("/api/v1/datasets/locations").then(function(p){state.locations=list(p.locations);renderLocations()}),
+  getJson("/api/v1/training/schema").then(function(p){state.trainingSchema=p||{trainers:[]};fillCreate()})
+]).catch(function(e){text("trainingReview",errorMessage(e))})}
+function datasetMatches(d){var q=($("datasetSearch").value||"").toLowerCase();if(q&&(d.name||"").toLowerCase().indexOf(q)<0)return false;if(state.locationFilter&&d.location_id!==state.locationFilter)return false;return true}
+function datasetImage(d){return d.thumbnail_url?img(d.thumbnail_url,"Dataset thumbnail"):document.createElement("span")}
+function useDataset(id){if(!id)return;postJson("/api/v1/datasets/"+encodeURIComponent(id)+"/use",{}).then(function(p){var d=p.dataset||{};state.selectedDataset=d.id||id;fillDatasets();if(!$("modelName").value)$("modelName").value=d.name||"";updateSelectedDatasetNote();switchView("createView");loadData()}).catch(function(e){text("datasetStats",errorMessage(e))})}
+function datasetCard(d,compact){var row=document.createElement("div");row.className="dataset-card"+(compact?" compact":"")+(d.available?"":" unavailable");var image=datasetImage(d);var body=document.createElement("div");var name=document.createElement("b");name.textContent=d.name||"Dataset";body.appendChild(name);var meta=document.createElement("div");meta.className="meta";meta.textContent=(d.available?String(d.image_count||d.item_count||0)+" images":"Unavailable")+" - "+(d.dataset_format||"Dataset");body.appendChild(meta);var star=document.createElement("button");star.type="button";star.className="icon";star.textContent=d.favorite?"*":"+";star.title=d.favorite?"Remove favorite":"Favorite";star.onclick=function(ev){ev.stopPropagation();postJson("/api/v1/datasets/"+encodeURIComponent(d.id)+"/favorite",{favorite:!d.favorite}).then(loadData)};row.appendChild(image);row.appendChild(body);row.appendChild(star);if(compact){row.onclick=function(){useDataset(d.id)};return row}var actions=document.createElement("div");actions.className="dataset-actions";var preview=document.createElement("button");preview.type="button";preview.className="ghost";preview.textContent="Preview";preview.onclick=function(){openDataset(d.id,1)};var use=document.createElement("button");use.type="button";use.className="primary";use.textContent="Use Dataset";use.onclick=function(){useDataset(d.id)};actions.appendChild(preview);actions.appendChild(use);row.appendChild(actions);return row}
+function fillList(id,items,empty,compact){var root=$(id);clear(root);items=items.filter(datasetMatches);if(!items.length){var e=document.createElement("div");e.className="status";e.textContent=empty;root.appendChild(e);return}items.forEach(function(d){root.appendChild(datasetCard(d,compact))})}
+function renderDatasets(){var all=state.datasets;fillList("favoriteDatasets",all.filter(function(d){return d.favorite}),"No favorites yet.",false);fillList("recentDatasets",all.filter(function(d){return d.last_used_at}),"Recently used datasets will appear here.",true);fillList("allDatasets",all,"No remembered datasets found. Add locations from desktop Training Studio.",false)}
+function renderLocations(){var chips=$("locationChips");clear(chips);var all=document.createElement("button");all.className="chip"+(state.locationFilter?"":" active");all.textContent="All locations";all.onclick=function(){state.locationFilter="";renderLocations();renderDatasets()};chips.appendChild(all);var listRoot=$("locationsList");var inlineRoot=$("locationsListInline");clear(listRoot);clear(inlineRoot);state.locations.forEach(function(loc){var c=document.createElement("button");c.className="chip"+(state.locationFilter===loc.id?" active":"");c.textContent=loc.name+(loc.available?"":" (unavailable)");c.onclick=function(){state.locationFilter=loc.id;renderLocations();renderDatasets()};chips.appendChild(c);var item=document.createElement("div");item.className="queue-item";appendText(item,"b",loc.name);appendText(item,"div",(loc.available?"available":"unavailable")+" - "+loc.source,"meta");listRoot.appendChild(item.cloneNode(true));inlineRoot.appendChild(item)})}
+function fillDatasets(){var current=$("trainDataset")?$("trainDataset").value:"";if(!$("trainDataset"))return;clear($("trainDataset"));state.datasets.filter(function(d){return d.available}).forEach(function(d){option($("trainDataset"),d.name+" ("+String(d.image_count||d.item_count||0)+")",d.id)});if(current)$("trainDataset").value=current;if(state.selectedDataset)$("trainDataset").value=state.selectedDataset;updateSelectedDatasetNote()}
+function updateSelectedDatasetNote(){var id=state.selectedDataset||($("trainDataset")&&$("trainDataset").value)||"";var d=state.datasets.filter(function(item){return item.id===id})[0];text("selectedDatasetNote",d?d.name+" - "+String(d.image_count||d.item_count||0)+" images":"No dataset selected")}
+function openDataset(id,page){state.selectedDataset=id;state.datasetPage=page;text("datasetStats","Loading preview...");$("datasetDetail").style.display="block";getJson("/api/v1/datasets/"+encodeURIComponent(id)+"/items?page="+encodeURIComponent(page)+"&page_size=24").then(function(p){var d=p.dataset||{};text("datasetTitle",d.name||"Dataset");text("datasetStats",String(d.image_count||d.item_count||0)+" items - "+String(d.caption_count||0)+" captions - "+String(d.missing_caption_count||0)+" missing captions - "+(d.dataset_format||"Dataset"));text("pageLabel","Page "+String((p.pagination||{}).page||page));$("prevPage").disabled=page<=1;$("nextPage").disabled=!(p.pagination||{}).has_next;var grid=$("datasetGrid");clear(grid);list(p.items).forEach(function(item){var b=document.createElement("button");b.type="button";b.className="thumb";var note=document.createElement("div");note.textContent=(item.display_name||"Image")+"\\n"+(item.decision||"unreviewed");b.appendChild(img(item.thumbnail_url,"Dataset image",note));b.appendChild(note);b.onclick=function(){showImage(item)};grid.appendChild(b)})}).catch(function(e){text("datasetStats",errorMessage(e))})}
+function showImage(item){state.selectedItem=item;$("imageDetail").style.display="block";$("detailImage").src=authUrl(item.preview_url);text("detailName",item.display_name||"Image");text("detailDims",String((item.dimensions||{}).width||0)+" x "+String((item.dimensions||{}).height||0));$("captionEditor").value=item.caption||"";text("imageStatus",item.decision||"unreviewed")}
+
+function fillCreate(){clear($("trainer"));list(state.trainingSchema.trainers).forEach(function(t){option($("trainer"),t.name,t.id)});clear($("preset"));option($("preset"),"Custom","");list(state.trainingSchema.presets).forEach(function(p){option($("preset"),p.name,p.id)});clear($("baseModel"));list(state.trainingSchema.base_models).forEach(function(m){option($("baseModel"),m.name,m.id)});fillDatasets();renderSettings();ensureEpochField()}
+function makeSetting(key,spec,advanced){if(key==="base_model")return null;var wrap=document.createElement("div");wrap.className=spec.type==="bool"?"toggle":"form-row";var input;if(spec.type==="bool"){var label=document.createElement("div");appendText(label,"b",spec.label||key);appendText(label,"div",spec.description||"Enable this setting","hint");input=document.createElement("input");input.type="checkbox";input.checked=spec.default!==false;wrap.appendChild(label);wrap.appendChild(input)}else{var label=document.createElement("label");label.textContent=spec.label||key;wrap.appendChild(label);if(spec.type==="choice"){input=document.createElement("select");list(spec.options).forEach(function(v){option(input,String(v),String(v))})}else{input=document.createElement("input");input.type=(spec.type==="int"||spec.type==="float"||spec.type==="slider")?"number":"text";if(spec.min!==undefined)input.min=spec.min;if(spec.max!==undefined)input.max=spec.max;if(spec.step!==undefined)input.step=spec.step}input.value=spec.default!==undefined?String(spec.default):"";wrap.appendChild(input)}input.id=fieldId(key);input.setAttribute("data-setting-key",key);input.setAttribute("data-setting-type",spec.type||"text");input.setAttribute("data-advanced",advanced?"1":"0");return wrap}
+function renderSettings(){var t=activeTrainer();var schema=t.settings||{};var basic=$("basicSettings"),advanced=$("advancedSettings");clear(basic);clear(advanced);var basicKeys=["resolution","batch_size","preview_enabled"];Object.keys(schema).forEach(function(key){var spec=schema[key]||{};var isAdvanced=!!spec.advanced||basicKeys.indexOf(key)<0;var node=makeSetting(key,spec,isAdvanced);if(!node)return;(isAdvanced?advanced:basic).appendChild(node)});$("baseModelWrap").style.display=schema.base_model?"grid":"none"}
+function settingValue(node){var type=node.getAttribute("data-setting-type");if(type==="bool")return !!node.checked;if(type==="int"||type==="slider")return Number(node.value||0);if(type==="float")return Number(node.value||0);var spec=schemaFor(node.getAttribute("data-setting-key"));if(spec.type==="choice"){var sample=list(spec.options)[0];if(typeof sample==="number")return Number(node.value)}return node.value}
+function trainingPayload(){var settings={};var nodes=document.querySelectorAll("[data-setting-key]");for(var i=0;i<nodes.length;i++){var key=nodes[i].getAttribute("data-setting-key");if(key==="epochs")continue;settings[key]=settingValue(nodes[i])}return{trainer:$("trainer").value,dataset_id:state.selectedDataset||$("trainDataset").value,base_model_id:$("baseModel").value,model_name:$("modelName").value,trigger_word:settings.trigger_word||"",epochs:Number($(fieldId("epochs"))&&$(fieldId("epochs")).value||10),settings:settings}}
+function applyPreset(){var id=$("preset").value;var preset=list(state.trainingSchema.presets).filter(function(p){return p.id===id})[0];if(!preset)return;if(preset.trainer){$("trainer").value=preset.trainer;renderSettings();ensureEpochField()}if(preset.epochs&&$(fieldId("epochs")))$(fieldId("epochs")).value=preset.epochs;Object.keys(preset.settings||{}).forEach(function(key){var n=$(fieldId(key));if(!n)return;if(n.type==="checkbox")n.checked=!!preset.settings[key];else n.value=String(preset.settings[key])})}
+function ensureEpochField(){if(!$(fieldId("epochs"))){var node=makeSetting("epochs",{label:"Epochs",type:"int",default:10,min:1,max:100000},false);$("basicSettings").appendChild(node)}}
+$("trainer").onchange=function(){renderSettings();ensureEpochField()};$("preset").onchange=applyPreset;$("datasetSearch").oninput=renderDatasets;$("filterDatasets").onclick=function(){var chips=$("locationChips");chips.style.display=chips.style.display==="none"?"flex":"none"};$("chooseDataset").onclick=function(){switchView("datasetsView")};$("createBack").onclick=function(){switchView("home")};$("createHelp").onclick=function(){text("trainingReview","Choose a preset, trainer, model name, and dataset. Advanced settings come from ADAM's trainer registry.")};$("askInstead").onclick=function(){switchView("home");$("prompt").focus()};$("useDataset").onclick=function(){useDataset(state.selectedDataset)};$("selectDatasetForCreate").onclick=$("useDataset").onclick;
+$("reviewPlan").onclick=function(){postJson("/api/v1/training/plan",trainingPayload()).then(function(p){$("trainingReview").textContent=JSON.stringify(p.plan||p,null,2)}).catch(function(e){text("trainingReview",errorMessage(e))})};
+$("startTraining").onclick=function(){if(!state.selectedDataset&&!$("trainDataset").value){text("trainingReview","Choose a dataset first.");return}if(!confirm("Start this training job on the PC?"))return;postJson("/api/v1/training/start",trainingPayload()).then(function(p){text("trainingReview",p.message||"Training queued.");load();loadData()}).catch(function(e){text("trainingReview",errorMessage(e))})};
+$("prevPage").onclick=function(){openDataset(state.selectedDataset,Math.max(1,state.datasetPage-1))};$("nextPage").onclick=function(){openDataset(state.selectedDataset,state.datasetPage+1)};
+$("saveCaption").onclick=function(){if(!state.selectedDataset||!state.selectedItem)return;postJson("/api/v1/datasets/"+encodeURIComponent(state.selectedDataset)+"/items/"+encodeURIComponent(state.selectedItem.id)+"/caption",{caption:$("captionEditor").value}).then(function(p){text("imageStatus",p.message||"Saved.")}).catch(function(e){text("imageStatus",errorMessage(e))})};
+function decide(decision){if(!state.selectedDataset||!state.selectedItem)return;postJson("/api/v1/datasets/"+encodeURIComponent(state.selectedDataset)+"/items/"+encodeURIComponent(state.selectedItem.id)+"/decision",{decision:decision}).then(function(p){text("imageStatus",p.message||decision);openDataset(state.selectedDataset,state.datasetPage)}).catch(function(e){text("imageStatus",errorMessage(e))})}
+$("keepImage").onclick=function(){decide("keep")};$("rejectImage").onclick=function(){decide("reject")};$("unreviewImage").onclick=function(){decide("unreviewed")};
+$("sendPrompt").onclick=function(){postJson("/api/prompt",{prompt:$("prompt").value}).then(function(p){text("promptStatus",p.message||"Sent.");$("prompt").value="";load()}).catch(function(e){text("promptStatus",errorMessage(e))})};$("askCreate").onclick=function(){switchView("createView")};$("askDataset").onclick=function(){$("prompt").value="Collect a new dataset for Example."};$("askQuick").onclick=function(){$("prompt").value="Generate an image of Example."};
+$("refresh").onclick=function(){load();loadData()};$("autoApproveTraining").onchange=function(e){postJson("/api/remote-settings",{auto_approve_training:e.target.checked}).then(function(p){text("settingsStatus",p.message||"Saved.");load()}).catch(function(err){text("settingsStatus",errorMessage(err))})};$("keepAwake").onchange=function(e){state.refreshMs=e.target.checked?1500:5000;startTimer();text("settingsStatus",e.target.checked?"Fast refresh is on.":"Quiet refresh is on.")};
+function startTimer(){if(timer)clearInterval(timer);timer=setInterval(load,state.refreshMs)}
+load();loadData();startTimer();
+}());
+</script>
+</body>
+</html>"""